@article{tran_2109,
title = {Digital Implementation of Homomorphically Encrypted Feedback Control for Cyber-Physical Systems},
author = {Tran, J.; Farokhi, F.; Cantoni, M.; Shames, I.},
year = {2109},
institution = {University of Melbourne, Australia},
abstract = {This paper is about an encryption based approach to the secure implementation of feedback controllers for cyberphysical systems. Specifically, Paillier’s homomorphic encryption is used in a custom digital implementation of a class of linear dynamic controllers, including static gain as a special case. The implementation is amenable to Field Programmable Gate Array (FPGA) realization. Experimental results, including timing analysis and resource usage characteristics for different encryption key lengths, are presented for the realization of a controller for an inverted pendulum; as this is an unstable plant, the control is necessarily fast. },
language = {English}
}

This paper is about an encryption based approach to the secure implementation of feedback controllers for cyberphysical systems. Specifically, Paillier’s homomorphic encryption is used in a custom digital implementation of a class of linear dynamic controllers, including static gain as a special case. The implementation is amenable to Field Programmable Gate Array (FPGA) realization. Experimental results, including timing analysis and resource usage characteristics for different encryption key lengths, are presented for the realization of a controller for an inverted pendulum; as this is an unstable plant, the control is necessarily fast.

@article{abdelkader_2018,
title = {Robust H ∞ gain neuro-adaptive observer design for nonlinear uncertain systems},
author = {Abdelkader, K.; Krais, B.},
journal = {Transactions of the Institute of Measurement and Control},
year = {2108},
institution = {Higher Institute of Computer Science of Mahdia, Tunisia},
abstract = {To guarantee convergent state estimates and exact approximations, it is highly desirable that observers can independently dominate the effects of unmodelled dynamics. Based on adaptive nonlinear approximation, this paper presents a robust H∞ gain neuro-adaptive observer (RH∞GNAO) design methodology for a large class of uncertain nonlinear systems in the presence of time-varying unknown parameters with bounded external disturbances on the state vector and on the output of the original system. The proposed RH∞GNAO incorporates radial basis function neural networks (RBFNNs) to approximate the unknown nonlinearities in the uncertain system. The weight dynamics of every RBFNN are adjusted online by using an adaptive projection algorithm. The asymptotic convergence of the state and parameter estimation errors is achieved by using Lyapunov cogitation under a well-defined persistent excitation condition, and without recourse to the strictly positive real condition. The repercussions of unknown disturbances are reduced by integrating an H∞ gain performance criterion into the proposed estimation approach. The condition imposed by this proposed observer approach, such that all estimated signals are uniformly ultimately bounded, is expressed in the form of the linear matrix inequality problem and warrants the demanded performances. To evaluate the performance of the proposed observer, various simulations are presented. },
keywords = {Uncertain nonlinear systems, Lyapunov stability, disturbances, neural networks, adaptive observer, H-infinity, linear matrix inequality},
language = {English},
publisher = {SAGE}
}

To guarantee convergent state estimates and exact approximations, it is highly desirable that observers can independently dominate the effects of unmodelled dynamics. Based on adaptive nonlinear approximation, this paper presents a robust H∞ gain neuro-adaptive observer (RH∞GNAO) design methodology for a large class of uncertain nonlinear systems in the presence of time-varying unknown parameters with bounded external disturbances on the state vector and on the output of the original system. The proposed RH∞GNAO incorporates radial basis function neural networks (RBFNNs) to approximate the unknown nonlinearities in the uncertain system. The weight dynamics of every RBFNN are adjusted online by using an adaptive projection algorithm. The asymptotic convergence of the state and parameter estimation errors is achieved by using Lyapunov cogitation under a well-defined persistent excitation condition, and without recourse to the strictly positive real condition. The repercussions of unknown disturbances are reduced by integrating an H∞ gain performance criterion into the proposed estimation approach. The condition imposed by this proposed observer approach, such that all estimated signals are uniformly ultimately bounded, is expressed in the form of the linear matrix inequality problem and warrants the demanded performances. To evaluate the performance of the proposed observer, various simulations are presented.

@article{kamal_2020,
title = {A New Class of Uniform Continuous Higher Order Sliding Mode Controllers},
author = {Kamal, S.; Ramesh Kumar, P.; Chalanga, A.; Kumar Goyal, J.; Bandyopadhyay, B.; Fridman, L.},
journal = {Journal of Dynamic Systems, Measurement, and Control},
year = {2020},
month = {01},
volume = {142},
number = {1},
institution = {Indian Institute of Technology (BHU) Varanasi, India; Government Engineering College Thrissur, India; University College London, UK; IIT Bombay, India; Universidad Nacional Aut ´onoma de M´exico (UNAM), Mexico },
abstract = {This paper proposes a new class of uniform continuous higher-order sliding mode algorithm (UCHOSMA) for the arbitrary relative degree systems. The proposed methodology is a combination of two controllers where one of the components is a uniform super-twisting control which acts as the disturbance compensator and the second part gives the uniform finite time convergence for the disturbance free system. This algorithm provides uniform finite time convergence of the output and its higher derivatives using an absolutely continuous control signal and thus alleviating the chattering phenomenon. The attractive feature of the proposed controller is that irrespective of the different initial conditions, the control is able to bring the states of the system to the equilibrium point uniformly in finite time. The effectiveness of the proposed controller has been demonstrated with both simulation and experimental results. },
language = {English},
publisher = {ASME}
}

This paper proposes a new class of uniform continuous higher-order sliding mode algorithm (UCHOSMA) for the arbitrary relative degree systems. The proposed methodology is a combination of two controllers where one of the components is a uniform super-twisting control which acts as the disturbance compensator and the second part gives the uniform finite time convergence for the disturbance free system. This algorithm provides uniform finite time convergence of the output and its higher derivatives using an absolutely continuous control signal and thus alleviating the chattering phenomenon. The attractive feature of the proposed controller is that irrespective of the different initial conditions, the control is able to bring the states of the system to the equilibrium point uniformly in finite time. The effectiveness of the proposed controller has been demonstrated with both simulation and experimental results.

@article{tosatto_2020,
title = {A Nonparametric Off-policy Policy Gradient},
author = {Tosatto, S.; Carvalho, J.; Abdulsamad, H.; Peters, J.},
journal = {arXiv},
year = {2020},
institution = {TU Darmstadt, Germany; Max Planck Institute for Intelligent Systems, Germany},
abstract = {Reinforcement learning (RL) algorithms still suffer from high sample complexity despite outstanding recent successes. The need for intensive interactions with the environment is especially observed in many widely popular policy gradient algorithms that perform updates using on-policy samples. The price of such inefficiency becomes evident in real-world scenarios such as interaction-driven robot learning, where the success of RL has been rather limited. We address this issue by building on the general sample efficiency of off-policy algorithms. With nonparametric regression and density estimation methods we construct a nonparametric Bellman equation in a principled manner, which allows us to obtain closed-form estimates of the value function, and to analytically express the full policy gradient. We provide a theoretical analysis of our estimate to show that it is consistent under mild smoothness assumptions and empirically show that our approach has better sample efficiency than state-of-the-art policy gradient methods. }
}

Reinforcement learning (RL) algorithms still suffer from high sample complexity despite outstanding recent successes. The need for intensive interactions with the environment is especially observed in many widely popular policy gradient algorithms that perform updates using on-policy samples. The price of such inefficiency becomes evident in real-world scenarios such as interaction-driven robot learning, where the success of RL has been rather limited. We address this issue by building on the general sample efficiency of off-policy algorithms. With nonparametric regression and density estimation methods we construct a nonparametric Bellman equation in a principled manner, which allows us to obtain closed-form estimates of the value function, and to analytically express the full policy gradient. We provide a theoretical analysis of our estimate to show that it is consistent under mild smoothness assumptions and empirically show that our approach has better sample efficiency than state-of-the-art policy gradient methods.

@article{kassem_2020,
title = {Active dynamic vibration absorber for flutter suppression},
author = {Kassem, M.; Yang, Z.; Gu, Y.; Wang, W.; Safwat, E.},
journal = {Journal of Sound and Vibration},
year = {2020},
month = {03},
volume = {469},
institution = {Military Technical College, Egypt; Northwestern Polytechnical University, China},
abstract = {A novel flutter suppression technique is proposed using an active dynamic vibration absorber (ADVA). The ADVA is introduced by adding an active element to a classical mass-spring-damper system. This active element drives the mass by a controlling force using feedback signal from the response of the aeroelastic system. An aeroelastic mathematical model of a 2 degrees of freedom (DOFs) airfoil equipped with the proposed ADVA is established based on unsteady aerodynamics theory. The proposed ADVA is experimentally realized by a cantilever beam with a bonded Macro-fiber Composite (MFC) and a lumped mass. MFC nonlinearities are compensated by applying Prandtl-Ishlinskii approach to design a proportional integral (PI)-based hysteresis compensator controller. A test rig is designed, fabricated and the parameters of the physical airfoil model and the connected ADVA are identified. The feasibility of the ADVA for flutter suppression is assessed via a wind tunnel testing. The experimental results show that applying the closed-loop ADVA increases the airfoil flutter speed by 42.9%. Whereas the open-loop ADVA results in 26.6% improvement in the flutter speed. Moreover, if the airfoil experiences a high amplitude limit cycle oscillation (LCO), the ADVA efficiently suppresses up to 93.3% of its amplitude when turned to the closed-loop control. },
keywords = {Flutter, LCO, Dynamic vibration absorber, Closed-loop control, Macro-fiber composite (MFC)},
language = {English},
publisher = {Elsevier Ltd.}
}

A novel flutter suppression technique is proposed using an active dynamic vibration absorber (ADVA). The ADVA is introduced by adding an active element to a classical mass-spring-damper system. This active element drives the mass by a controlling force using feedback signal from the response of the aeroelastic system. An aeroelastic mathematical model of a 2 degrees of freedom (DOFs) airfoil equipped with the proposed ADVA is established based on unsteady aerodynamics theory. The proposed ADVA is experimentally realized by a cantilever beam with a bonded Macro-fiber Composite (MFC) and a lumped mass. MFC nonlinearities are compensated by applying Prandtl-Ishlinskii approach to design a proportional integral (PI)-based hysteresis compensator controller. A test rig is designed, fabricated and the parameters of the physical airfoil model and the connected ADVA are identified. The feasibility of the ADVA for flutter suppression is assessed via a wind tunnel testing. The experimental results show that applying the closed-loop ADVA increases the airfoil flutter speed by 42.9%. Whereas the open-loop ADVA results in 26.6% improvement in the flutter speed. Moreover, if the airfoil experiences a high amplitude limit cycle oscillation (LCO), the ADVA efficiently suppresses up to 93.3% of its amplitude when turned to the closed-loop control.

@conference{halverson_2020,
title = {Attitude Control of a Spacecraft Flexible Appendage using Parallel Feedforward Control},
author = {Halverson, R.D.; Caverly, R.},
booktitle = {AIAA Scitech 2020 Forum},
year = {2020},
institution = {University of Minnesota, USA},
abstract = {In this paper, static and dynamic strictly positive real (SPR) parallel feedforward control methods are applied to a spacecraft with a large payload attached to the end of a flexible appendage. A dynamic model of this spacecraft is considered with a torque applied directly to the hub of the spacecraft, where the control objective is to track a desired angular velocity of the payload. This setup leads to a noncolocated relationship between the spacecraft hub torque input and the payload angular velocity output. Numerical simulation results demonstrate that the parallel feedforward controller successfully renders the system SPR, which simplifies the choice of a stabilizing feedback controller. It is shown that parallel feedforward control significantly increases the effective gain of the feedback controller within a specified frequency bandwidth. These results are expanded to an experimental rotary flexible joint manipulator, which is used as a physical analog to the flexible-appendage spacecraft. The experimental results confirm the findings from the numerical results, demonstrating the practical nature of the proposed control method. Both numerical and experimental results include a comparison to the state-of-the-art mu-tip control method that relies on a massive payload assumption, where it is shown that parallel feedforward control is implementable in situations where mu-tip control is not. },
language = {English},
publisher = {AIAA}
}

In this paper, static and dynamic strictly positive real (SPR) parallel feedforward control methods are applied to a spacecraft with a large payload attached to the end of a flexible appendage. A dynamic model of this spacecraft is considered with a torque applied directly to the hub of the spacecraft, where the control objective is to track a desired angular velocity of the payload. This setup leads to a noncolocated relationship between the spacecraft hub torque input and the payload angular velocity output. Numerical simulation results demonstrate that the parallel feedforward controller successfully renders the system SPR, which simplifies the choice of a stabilizing feedback controller. It is shown that parallel feedforward control significantly increases the effective gain of the feedback controller within a specified frequency bandwidth. These results are expanded to an experimental rotary flexible joint manipulator, which is used as a physical analog to the flexible-appendage spacecraft. The experimental results confirm the findings from the numerical results, demonstrating the practical nature of the proposed control method. Both numerical and experimental results include a comparison to the state-of-the-art mu-tip control method that relies on a massive payload assumption, where it is shown that parallel feedforward control is implementable in situations where mu-tip control is not.

@article{frohlich_2020,
title = {Bayesian Optimization for Policy Search in High-Dimensional Systems via Automatic Domain Selection},
author = {Fröhlich, L.P.; Klenske, E.D.; Daniel, C.G.; Zeilinger, M.N.},
journal = {arXiv},
year = {2020},
institution = {Bosch Center for Artificial Intelligence, Germany; ETH Zurich, Switzerland},
abstract = {Bayesian Optimization (BO) is an effective method for optimizing expensive-to-evaluate black-box functions with a wide range of applications for example in robotics, system design and parameter optimization. However, scaling BO to problems with large input dimensions (>10) remains an open challenge. In this paper, we propose to leverage results from optimal control to scale BO to higher dimensional control tasks and to reduce the need for manually selecting the optimization domain. The contributions of this paper are twofold: 1) We show how we can make use of a learned dynamics model in combination with a model-based controller to simplify the BO problem by focusing onto the most relevant regions of the optimization domain. 2) Based on (1) we present a method to find an embedding in parameter space that reduces the effective dimensionality of the optimization problem. To evaluate the effectiveness of the proposed approach, we present an experimental evaluation on real hardware, as well as simulated tasks including a 48-dimensional policy for a quadcopter. },
language = {English}
}

Bayesian Optimization (BO) is an effective method for optimizing expensive-to-evaluate black-box functions with a wide range of applications for example in robotics, system design and parameter optimization. However, scaling BO to problems with large input dimensions (>10) remains an open challenge. In this paper, we propose to leverage results from optimal control to scale BO to higher dimensional control tasks and to reduce the need for manually selecting the optimization domain. The contributions of this paper are twofold: 1) We show how we can make use of a learned dynamics model in combination with a model-based controller to simplify the BO problem by focusing onto the most relevant regions of the optimization domain. 2) Based on (1) we present a method to find an embedding in parameter space that reduces the effective dimensionality of the optimization problem. To evaluate the effectiveness of the proposed approach, we present an experimental evaluation on real hardware, as well as simulated tasks including a 48-dimensional policy for a quadcopter.

@article{shahid_2020,
title = {Comparative Analysis of Different Model-Based Controllers Using Active Vehicle Suspension System},
author = {Shahid, Y.; Wei, M.},
journal = {Algorithms},
year = {2020},
institution = {Nanjing University of Aeronautics and Astronautics, China},
abstract = {This paper deals with the active vibration control of a quarter-vehicle suspension system. Damping control methods investigated in this paper are: higher-order sliding mode control (HOSMC) based on super twisting algorithm (STA), first-order sliding mode control (FOSMC), integral sliding mode control (ISMC), proportional integral derivative (PID), linear quadratic regulator (LQR) and passive suspension system. Performance comparison of different active controllers are analyzed in terms of vertical displacement, suspension travel and wheel deflection. The theoretical, quantitative and qualitative analysis verify that the STA-based HOSMC exhibits better performance as well as negate the undesired disturbances with respect to FOSMC, ISMC, PID, LQR and passive suspension system. Furthermore, it is also robust to intrinsic bounded uncertain dynamics of the model. },
issn = {1999-4893},
keywords = {higher-order sliding mode control; quarter-car; super twisting algorithm; active suspension system},
language = {English},
publisher = {MDPI AG}
}

This paper deals with the active vibration control of a quarter-vehicle suspension system. Damping control methods investigated in this paper are: higher-order sliding mode control (HOSMC) based on super twisting algorithm (STA), first-order sliding mode control (FOSMC), integral sliding mode control (ISMC), proportional integral derivative (PID), linear quadratic regulator (LQR) and passive suspension system. Performance comparison of different active controllers are analyzed in terms of vertical displacement, suspension travel and wheel deflection. The theoretical, quantitative and qualitative analysis verify that the STA-based HOSMC exhibits better performance as well as negate the undesired disturbances with respect to FOSMC, ISMC, PID, LQR and passive suspension system. Furthermore, it is also robust to intrinsic bounded uncertain dynamics of the model.

@article{guo_2020,
title = {Composite learning control of robotic systems: A least squares modulated approach},
author = {Guo, K.; Pan, Y.; Zheng, D.; Yu, H.},
journal = {Automatica},
year = {2020},
month = {01},
volume = {111},
institution = {Shandong University, China; Sun Yat-sen University, China; National University of Singapore, Singapore},
abstract = {Most current studies of adaptive robot control concentrate on parameter convergence in the steady state, while parameter convergence rates are rarely investigated. This paper proposes a least-squares modulated composite learning robot control based on Moore–Penrose pseudoinverse to improve the performance of parameter convergence. In the composite learning, a prediction error is constructed based on online historical data and regressor extension, and both the prediction and tracking errors are applied to update parameter estimates such that accurate and smooth parameter estimation is obtained under a weak excitation condition termed interval excitation (IE). The distinctive features of the proposed method include: (1) Asymptotic stability of the closed-loop system is proven without the IE condition; (2) exponential stability is proven and balanced and easily tunable rates of parameter convergence are achieved under the IE condition, where the rates are independent of unpredictable excitation levels in different regressor channels. These two features are generally not achievable with the existing adaptive robot control methods. Experimental results on an industrial manipulator have demonstrated the effectiveness and superiority of the proposed approach. },
keywords = {Adaptive control, Composite learning, Least squares, Moore–Penrose pseudoinverse, Robot manipulators},
language = {English},
publisher = {Elsevier Ltd.}
}

Most current studies of adaptive robot control concentrate on parameter convergence in the steady state, while parameter convergence rates are rarely investigated. This paper proposes a least-squares modulated composite learning robot control based on Moore–Penrose pseudoinverse to improve the performance of parameter convergence. In the composite learning, a prediction error is constructed based on online historical data and regressor extension, and both the prediction and tracking errors are applied to update parameter estimates such that accurate and smooth parameter estimation is obtained under a weak excitation condition termed interval excitation (IE). The distinctive features of the proposed method include: (1) Asymptotic stability of the closed-loop system is proven without the IE condition; (2) exponential stability is proven and balanced and easily tunable rates of parameter convergence are achieved under the IE condition, where the rates are independent of unpredictable excitation levels in different regressor channels. These two features are generally not achievable with the existing adaptive robot control methods. Experimental results on an industrial manipulator have demonstrated the effectiveness and superiority of the proposed approach.

@article{kong_2020,
title = {Contraction analysis of nonlinear noncausal iterative learning control},
author = {Kong, F.H.; Manchester, I.R.},
journal = {Systems & Control Letters},
year = {2020},
month = {02},
volume = {136},
institution = {Universty of Sydney, Australia},
abstract = {Iterative learning control (ILC) is a method for learning input signals for repetitive control tasks. In this paper, we provide a new method based on convex optimization for certifying convergence and estimating convergence rate in ILC schemes involving a nonlinear plant and a noncausal update law, which are common in practice. Using sum-of-squares (SOS) optimization, we compute the convergence rate of an example nonlinear, noncausal ILC system and verify its accuracy in experiment. },
keywords = {Iterative learning control, Contraction analysis, Linear matrix inequalities},
language = {English},
publisher = {Elsevier B.V.}
}

Iterative learning control (ILC) is a method for learning input signals for repetitive control tasks. In this paper, we provide a new method based on convex optimization for certifying convergence and estimating convergence rate in ILC schemes involving a nonlinear plant and a noncausal update law, which are common in practice. Using sum-of-squares (SOS) optimization, we compute the convergence rate of an example nonlinear, noncausal ILC system and verify its accuracy in experiment.

@article{oliveira_2020,
title = {Design and experiments on an inflatable link robot with a built-in vision sensor},
author = {Oliveira, J.; Ferreira, A.; Reis, J.C.P.},
journal = {Mechatronics},
year = {2020},
month = {02},
volume = {65},
institution = {Universidade de Lisboa, Portugal},
abstract = {Soft robots and manipulators with inflatable links possess inherent safety characteristics that make them a very interesting choice for tasks requiring human-robot interaction. However, they may face important challenges in their performance due to low structural stiffness. Accurate positioning of the end effector may prove a difficult task due to limitations in gravity compensation, or due to the occurrence of oscillations during motion, especially in the absence of precise information about the payload that is being manipulated. In this paper a novel approach to the design and control of a robot with an inflatable link is proposed, where the link assumes the triple role of compliant structural element, touch sensor, and active mechanism to adjust the performance of vibration control algorithms. For this, a vision sensor is placed inside the link to provide information about the structural deformation. The sensor is used to measure the tip displacement, and also to detect contact of the link surface with the surrounding environment. The concept was implemented on a two-joint rigid manipulator with a single inflatable link. Experiments on vibration control and contact detection of the inflatable link are reported where the control system was able to significantly reduce tip oscillations, including when a payload was added to the tip. The robot was also able to make binary detection of contact between the inflatable link and the user, and change its operation mode accordingly. },
keywords = {Inflatable robots, Soft material robotics, Flexible robots, Physical human-robot interaction},
language = {English},
publisher = {Elsevier B.V.}
}

Soft robots and manipulators with inflatable links possess inherent safety characteristics that make them a very interesting choice for tasks requiring human-robot interaction. However, they may face important challenges in their performance due to low structural stiffness. Accurate positioning of the end effector may prove a difficult task due to limitations in gravity compensation, or due to the occurrence of oscillations during motion, especially in the absence of precise information about the payload that is being manipulated. In this paper a novel approach to the design and control of a robot with an inflatable link is proposed, where the link assumes the triple role of compliant structural element, touch sensor, and active mechanism to adjust the performance of vibration control algorithms. For this, a vision sensor is placed inside the link to provide information about the structural deformation. The sensor is used to measure the tip displacement, and also to detect contact of the link surface with the surrounding environment. The concept was implemented on a two-joint rigid manipulator with a single inflatable link. Experiments on vibration control and contact detection of the inflatable link are reported where the control system was able to significantly reduce tip oscillations, including when a payload was added to the tip. The robot was also able to make binary detection of contact between the inflatable link and the user, and change its operation mode accordingly.

@article{armenta_2020,
title = {Exact Takagi-Sugeno descriptor models of recurrent high-order neural networks for control applications},
author = {Armenta, C.; Bernal, M.; Estrada-Manzo, V.; Sala, A.},
journal = {Computational and Applied Mathematics},
year = {2020},
month = {03},
volume = {39},
institution = {Sonora Institute of Technology, Mexico; Universidad Politécnica de Pachuca, Mexico; Universitat Politecnica de Valencia, Spain},
abstract = {This work presents an exact Takagi-Sugeno descriptor model of a recurrent high-order neural network arising from identification of a nonlinear plant. The proposed rearrangement allows exploiting the nonlinear characteristics of the neural model for H∞-optimal controller design whose conditions are expressed as linear matrix inequalities. Simulation and real-time results are presented that illustrate the advantages of the proposal. },
issn = {2238-3603},
keywords = {Descriptor system, Linear matrix inequality, Takagi-Sugeno model, Recurrent high-order neural network},
language = {English},
publisher = {Springer International Publishing}
}

This work presents an exact Takagi-Sugeno descriptor model of a recurrent high-order neural network arising from identification of a nonlinear plant. The proposed rearrangement allows exploiting the nonlinear characteristics of the neural model for H∞-optimal controller design whose conditions are expressed as linear matrix inequalities. Simulation and real-time results are presented that illustrate the advantages of the proposal.

@inbook{yazdjerdi_2020,
title = {Fault Tolerant Controller Schemes for Single and Multiple Mobile Robots},
author = {Yazdjerdi, P.; Meskin N. },
booktitle = {Soft Computing in Condition Monitoring and Diagnostics of Electrical and Mechanical Systems},
year = {2020},
institution = {Qatar University, Qatar},
abstract = {In this chapter, an actuator fault tolerant controller is developed for both single and multiple differential drive mobile robots. First, a fault tolerant controller is proposed for loss of effectiveness actuator faults in differential drive mobile robots while tracking the desired trajectory. The actuator loss of effectiveness fault is injected on the kinematic equation of the robot as a multiplicative gain in the left and right wheels angular velocity. Accordingly, the goal is to estimate and tolerate the injected actuator faults. A fault diagnosis method based on joint state and parameter estimation scheme is proposed to estimate the actuator loss of effectiveness gains as well as the states of the system. The estimated values of actuator faults are then used in the controller to compensate their effects on the mobile robots performance. Next, the proposed Fault Tolerant Controller (FTC) method is extended for the leader–follower formation control of mobile robots in the presence of actuator fault in a leader and followers robots. An Extended Kalman Filter (EKF) is utilized for each robot to obtain the parameters and states of the system and as the actuator fault is detected in any of the robots, the corresponding controller compensates the fault. Finally, an obstacle avoidance feature is added to the designed actuator fault tolerant controller, for single mobile robot. Toward this goal, a go-to goal controller is designed for the mobile robot to reach the desired destination. Meanwhile, the robot can detect any obstacle with a given color specification using Kinect sensor mounted on the robot and consequently avoid collisions. The efficacy of the proposed FTC framework is demonstrated for both single and multiple mobile robots by real-time simulation and implementation on Qbot-2 from Quanser. },
language = {English},
series = {Advances in Intelligent Systems and Computing},
publisher = {Springer, Singapore},
isbn = {978-981-15-1532-3}
}

In this chapter, an actuator fault tolerant controller is developed for both single and multiple differential drive mobile robots. First, a fault tolerant controller is proposed for loss of effectiveness actuator faults in differential drive mobile robots while tracking the desired trajectory. The actuator loss of effectiveness fault is injected on the kinematic equation of the robot as a multiplicative gain in the left and right wheels angular velocity. Accordingly, the goal is to estimate and tolerate the injected actuator faults. A fault diagnosis method based on joint state and parameter estimation scheme is proposed to estimate the actuator loss of effectiveness gains as well as the states of the system. The estimated values of actuator faults are then used in the controller to compensate their effects on the mobile robots performance. Next, the proposed Fault Tolerant Controller (FTC) method is extended for the leader–follower formation control of mobile robots in the presence of actuator fault in a leader and followers robots. An Extended Kalman Filter (EKF) is utilized for each robot to obtain the parameters and states of the system and as the actuator fault is detected in any of the robots, the corresponding controller compensates the fault. Finally, an obstacle avoidance feature is added to the designed actuator fault tolerant controller, for single mobile robot. Toward this goal, a go-to goal controller is designed for the mobile robot to reach the desired destination. Meanwhile, the robot can detect any obstacle with a given color specification using Kinect sensor mounted on the robot and consequently avoid collisions. The efficacy of the proposed FTC framework is demonstrated for both single and multiple mobile robots by real-time simulation and implementation on Qbot-2 from Quanser.

@article{ren_2020,
title = {Learning inverse kinematics and dynamics of a robotic manipulator using generative adversarial networks},
author = {Ren, H.; Ben-Tzvi, P.},
journal = {Robotics and Autonomous Systems},
year = {2020},
month = {02},
volume = {124},
institution = {Virginia Tech, USA},
abstract = {Obtaining inverse kinematics and dynamics of a robotic manipulator is often crucial for robot control. Analytical models are typically used to approximate real robot systems, and various controllers have been designed on top of the analytical model to compensate for the approximation error. Recently, machine learning techniques have been developed for error compensation, resulting in better performance. Unfortunately, combining a learned compensator with an analytical model makes the designed controller redundant and computationally expensive. Also, general machine learning techniques require a lot of data to perform the training process and approximation, especially in solving high dimensional problems. As a result, state-of-the-art machine learning applications are either expensive in terms of computation and data collection, or limited to a local approximation for a specific task or routine. In order to address the high dimensionality problem in learning inverse kinematics and dynamics, as well as to make the training process more data efficient, this paper presents a novel approach using a series of modified Generative Adversarial Networks (GANs). Namely, we use Conditional GANs (CGANs), Least Squares GANs (LSGANs), Bidirectional GANs (BiGANs) and Dual GANs(DualGANs). We trained and tested the proposed methods using real-world data collected from two types of robotic manipulators, a MICO robotic manipulator and a Fetch robotic manipulator. The data input to the GANs was obtained using a sampling method applied to the real data. The proposed approach enables approximating the real model using limited data without compromising the performance and accuracy. The proposed methods were tested in real-world experiments using unseen trajectories to validate the “learned” approximate inverse kinematics and inverse dynamics as well as to demonstrate the capability and effectiveness of the proposed algorithm over existing analytical models. },
keywords = {Inverse kinematics, Inverse dynamics, Generative adversarial networks},
language = {English},
publisher = {Elsevier B.V.}
}

Obtaining inverse kinematics and dynamics of a robotic manipulator is often crucial for robot control. Analytical models are typically used to approximate real robot systems, and various controllers have been designed on top of the analytical model to compensate for the approximation error. Recently, machine learning techniques have been developed for error compensation, resulting in better performance. Unfortunately, combining a learned compensator with an analytical model makes the designed controller redundant and computationally expensive. Also, general machine learning techniques require a lot of data to perform the training process and approximation, especially in solving high dimensional problems. As a result, state-of-the-art machine learning applications are either expensive in terms of computation and data collection, or limited to a local approximation for a specific task or routine. In order to address the high dimensionality problem in learning inverse kinematics and dynamics, as well as to make the training process more data efficient, this paper presents a novel approach using a series of modified Generative Adversarial Networks (GANs). Namely, we use Conditional GANs (CGANs), Least Squares GANs (LSGANs), Bidirectional GANs (BiGANs) and Dual GANs(DualGANs). We trained and tested the proposed methods using real-world data collected from two types of robotic manipulators, a MICO robotic manipulator and a Fetch robotic manipulator. The data input to the GANs was obtained using a sampling method applied to the real data. The proposed approach enables approximating the real model using limited data without compromising the performance and accuracy. The proposed methods were tested in real-world experiments using unseen trajectories to validate the “learned” approximate inverse kinematics and inverse dynamics as well as to demonstrate the capability and effectiveness of the proposed algorithm over existing analytical models.

@article{wiley_2020,
title = {Magneto-rheological actuator with permanent magnets for low-power activation},
author = {Wiley, B.; Gurocak, H.},
journal = {Journal of Intelligent Material Systems and Structures},
year = {2020},
institution = {Washington State University, USA},
abstract = {This research presents a new magneto-rheological brake that uses only permanent magnets to produce variable torque output. A new magnet array was developed that can be operated by a tiny motor allowing the device to apply 1.8 Nm torque using only 1.4 W of electrical power. In many applications, it is desirable to have actuators with high torque output, low volume, and low input power. In recent years, this has led to extensive study of magneto-rheological brakes since these devices can provide high torque-to-volume ratios when compared to electric motors of similar size. There have been many studies to optimize volume and torque, which usually leads to designs requiring complex internal magnetic flux paths and excessive power requirement resulting in joule heating and reduced performance in long-term use. Our brake can operate for 22 h using a small battery while cycling through full on/off torque output. It can also apply full torque output indefinitely with no power input required to keep it on. There is no joule heating since there are no coils in the design. },
issn = {1530-8138},
keywords = {Magneto-rheological brake, magneto-rheological fluid, MRF, MRB, permanent magnet array, Halbach array, haptics, robotics},
language = {English},
publisher = {SAGE Publications}
}

This research presents a new magneto-rheological brake that uses only permanent magnets to produce variable torque output. A new magnet array was developed that can be operated by a tiny motor allowing the device to apply 1.8 Nm torque using only 1.4 W of electrical power. In many applications, it is desirable to have actuators with high torque output, low volume, and low input power. In recent years, this has led to extensive study of magneto-rheological brakes since these devices can provide high torque-to-volume ratios when compared to electric motors of similar size. There have been many studies to optimize volume and torque, which usually leads to designs requiring complex internal magnetic flux paths and excessive power requirement resulting in joule heating and reduced performance in long-term use. Our brake can operate for 22 h using a small battery while cycling through full on/off torque output. It can also apply full torque output indefinitely with no power input required to keep it on. There is no joule heating since there are no coils in the design.

@article{wu_2020,
title = {Memristor Hardware-Friendly Reinforcement Learning},
author = {Wu, N.; Vincent,A.; Strukov, D.; Xie,Y.},
journal = {arXiv},
year = {2020},
abstract = {Recently, significant progress has been made in solving sophisticated problems among various domains by using reinforcement learning (RL), which allows machines or agents to learn from interactions with environments rather than explicit supervision. As the end of Moore's law seems to be imminent, emerging technologies that enable high performance neuromorphic hardware systems are attracting increasing attention. Namely, neuromorphic architectures that leverage memristors, the programmable and nonvolatile two-terminal devices, as synaptic weights in hardware neural networks, are candidates of choice to realize such highly energy-efficient and complex nervous systems. However, one of the challenges for memristive hardware with integrated learning capabilities is prohibitively large number of write cycles that might be required during learning process, and this situation is even exacerbated under RL situations. In this work we propose a memristive neuromorphic hardware implementation for the actor-critic algorithm in RL. By introducing a two-fold training procedure (i.e., ex-situ pre-training and in-situ re-training) and several training techniques, the number of weight updates can be significantly reduced and thus it will be suitable for efficient in-situ learning implementations. As a case study, we consider the task of balancing an inverted pendulum, a classical problem in both RL and control theory. We believe that this study shows the promise of using memristor-based hardware neural networks for handling complex tasks through in-situ reinforcement learning. },
keywords = {Artificial neural networks, Reinforcement learning, Memristor, ReRAM, In-situ training, Hardware implementation, Actor-Critic},
language = {English}
}

Recently, significant progress has been made in solving sophisticated problems among various domains by using reinforcement learning (RL), which allows machines or agents to learn from interactions with environments rather than explicit supervision. As the end of Moore's law seems to be imminent, emerging technologies that enable high performance neuromorphic hardware systems are attracting increasing attention. Namely, neuromorphic architectures that leverage memristors, the programmable and nonvolatile two-terminal devices, as synaptic weights in hardware neural networks, are candidates of choice to realize such highly energy-efficient and complex nervous systems. However, one of the challenges for memristive hardware with integrated learning capabilities is prohibitively large number of write cycles that might be required during learning process, and this situation is even exacerbated under RL situations. In this work we propose a memristive neuromorphic hardware implementation for the actor-critic algorithm in RL. By introducing a two-fold training procedure (i.e., ex-situ pre-training and in-situ re-training) and several training techniques, the number of weight updates can be significantly reduced and thus it will be suitable for efficient in-situ learning implementations. As a case study, we consider the task of balancing an inverted pendulum, a classical problem in both RL and control theory. We believe that this study shows the promise of using memristor-based hardware neural networks for handling complex tasks through in-situ reinforcement learning.

@article{razmi_2020,
title = {Near-Optimal Neural-Network Robot Control with Adaptive Gravity Compensation},
author = {Razmi, M.; Macnab, C.J.B.},
journal = {Neurocomputing},
year = {2020},
institution = {University of Calgary, Canada},
abstract = {Adaptive nonlinear optimal control methods, as proposed in the literature, give rise to some questions around practical implementation in robotics, especially how to find a solution in a reasonable time and how to deal with gravity. This paper proposes a method to solve these problems by using a neural network with local basis-function domains, specifically the Cerebellar Model Articulation Controller (CMAC). The algorithm uses the local domains in order to keep track of the value of local cost-functionals. In this way, it can freeze the learning of the network’s weights in a feedforward-like component in the CMAC when the bias has been overcome identified by using an error-based cost-functional e.g. automatic gravity compensation in a robot. After the feedforward component has been established, the algorithm then starts to learn another set of weights which contribute to feedback-like terms in the CMAC and these weights get frozen when they no longer reduce a cost-functional that includes additional control effort e.g. in a robot the control effort beyond that needed to compensate for gravity is penalized. Lyapunov methods guarantee uniformly ultimately bounded signals and ensure weight drift and bursting do not occur. One advantage is that the training time for finding a near-optimal control does not increase over previous neural-adaptive methods. Another advantage is that penalizing the control effort in a search for optimization does result in any steady-state error due to gravity. Simulations show that the proposed method significantly outperforms a standard adaptive-CMAC control using e-modification, without increasing control effort or training time. An experimental flexible-joint robot verifies that the adaptive method significantly outperforms a linear quadratic regulator. },
keywords = {nonlinear optimal control direct adaptive control neural-adaptive control overlearning weight drift bursting Cerebellar Model Articulation Controller elastic-joint robot},
language = {English},
publisher = {Elsevier B.V.}
}

Adaptive nonlinear optimal control methods, as proposed in the literature, give rise to some questions around practical implementation in robotics, especially how to find a solution in a reasonable time and how to deal with gravity. This paper proposes a method to solve these problems by using a neural network with local basis-function domains, specifically the Cerebellar Model Articulation Controller (CMAC). The algorithm uses the local domains in order to keep track of the value of local cost-functionals. In this way, it can freeze the learning of the network’s weights in a feedforward-like component in the CMAC when the bias has been overcome identified by using an error-based cost-functional e.g. automatic gravity compensation in a robot. After the feedforward component has been established, the algorithm then starts to learn another set of weights which contribute to feedback-like terms in the CMAC and these weights get frozen when they no longer reduce a cost-functional that includes additional control effort e.g. in a robot the control effort beyond that needed to compensate for gravity is penalized. Lyapunov methods guarantee uniformly ultimately bounded signals and ensure weight drift and bursting do not occur. One advantage is that the training time for finding a near-optimal control does not increase over previous neural-adaptive methods. Another advantage is that penalizing the control effort in a search for optimization does result in any steady-state error due to gravity. Simulations show that the proposed method significantly outperforms a standard adaptive-CMAC control using e-modification, without increasing control effort or training time. An experimental flexible-joint robot verifies that the adaptive method significantly outperforms a linear quadratic regulator.

@inbook{taskin_2020,
title = {Robust and Intelligent Control Techniques for Twin Rotor System},
author = {Taskin, Y.; Hacioglu, Y.},
booktitle = {Handbook of Research on Advanced Mechatronic Systems and Intelligent Robotics},
year = {2020},
institution = {Istanbul University, Turkey},
abstract = {During recent decades, popularity of unmanned aerial vehicles has increased throughout academics, engineers, as well as students because of their wide range of application areas such as inspection, communication, and transportation. The twin rotor system represents the nonlinear and coupled dynamics of a helicopter to a certain extent. Therefore, it provides a good test bed for engineering students’ education on dynamics of aerial vehicles and control of mechatronic systems. In this study, sliding mode and fuzzy logic controllers are designed to control hovering motion of this highly nonlinear system. A robust sliding mode controller is preferred since it is insensitive to external disturbances, and intelligent fuzzy logic control is preferred since it is applicable to nonlinear systems where exact model of the system is not needed. Designed controllers, which are robust and intelligent, are then applied to the twin rotor system in real time. Then, experimental results are presented and discussed in terms of control performances of the designed controllers in detail. },
language = {English},
pages = {401-420}
}

During recent decades, popularity of unmanned aerial vehicles has increased throughout academics,
engineers, as well as students because of their wide range of application areas such as inspection, communication,
and transportation. The twin rotor system represents the nonlinear and coupled dynamics of
a helicopter to a certain extent. Therefore, it provides a good test bed for engineering students’ education
on dynamics of aerial vehicles and control of mechatronic systems. In this study, sliding mode and fuzzy
logic controllers are designed to control hovering motion of this highly nonlinear system. A robust sliding
mode controller is preferred since it is insensitive to external disturbances, and intelligent fuzzy logic
control is preferred since it is applicable to nonlinear systems where exact model of the system is not
needed. Designed controllers, which are robust and intelligent, are then applied to the twin rotor system
in real time. Then, experimental results are presented and discussed in terms of control performances
of the designed controllers in detail.

@article{wang_2019,
title = {A computationally efficient dynamical model of fluidic soft actuators and its experimental verification},
author = {Wang, T.; Zhang, Y.; Zhu, Y.; Zhu, S.},
journal = {Mechatronics},
year = {2019},
month = {04},
volume = {58},
institution = {Zhejiang University, China},
abstract = {This work develops a computationally efficient dynamical model for fluidic bending soft actuators in consideration of distributed-mass effect. The model in universal form is based on the Lagrangian approach and the constant curvature assumption. Since the model parameters are dependent on actuator type, a case study on the soft actuator with fiber-reinforced structure is carried out due to its convenience in manufacture and resistance to trivial deformation. The expressions of strain energy, gravitational potential energy, kinetic energy and generalized force are derived with respect to a generalized coordinate defined by the bending angle of the soft actuator. Material deformation and fluidic volume variation are also considered and derived in the model. Additionally, Taylor series expansions are employed to reduce the model complexity by neglecting the high-order terms reasonably. Finally, an experimental setup for the soft actuator is built and a curvature sensor is utilized to detect the bending angle. Experiments under typical operating conditions are implemented to verify the model and the results show that the measured data are in good accordance with the predicted responses. The proposed dynamical model can serve as a basis for further model-based control algorithm design for fluidic soft actuators. },
keywords = {Dynamical model Bending actuators Computationally efficient Fluidic power Soft robotics},
language = {English},
publisher = {Elsevier Ltd.}
}

This work develops a computationally efficient dynamical model for fluidic bending soft actuators in consideration of distributed-mass effect. The model in universal form is based on the Lagrangian approach and the constant curvature assumption. Since the model parameters are dependent on actuator type, a case study on the soft actuator with fiber-reinforced structure is carried out due to its convenience in manufacture and resistance to trivial deformation. The expressions of strain energy, gravitational potential energy, kinetic energy and generalized force are derived with respect to a generalized coordinate defined by the bending angle of the soft actuator. Material deformation and fluidic volume variation are also considered and derived in the model. Additionally, Taylor series expansions are employed to reduce the model complexity by neglecting the high-order terms reasonably. Finally, an experimental setup for the soft actuator is built and a curvature sensor is utilized to detect the bending angle. Experiments under typical operating conditions are implemented to verify the model and the results show that the measured data are in good accordance with the predicted responses. The proposed dynamical model can serve as a basis for further model-based control algorithm design for fluidic soft actuators.

@article{zakerimanesh2_2019,
title = {A cooperative paradigm for task-space control of multilateral nonlinear teleoperation with bounded inputs and time-varying delays},
author = {Zakerimanesh, A.; Hashemzadeh, F.; Torabi, A.; Tavakoli, M.},
journal = {Mechatronics},
year = {2019},
month = {10},
volume = {62},
institution = {University of Tabriz, Iran; University of Alberta, Canada},
abstract = {In this article, a novel multilateral teleoperation structure is presented for cooperative control of a redundant remote robot in the task-space. In the structure, each local robot is assigned a real number between zero and one, as its dominance factor. The sum of all the assigned factors is equal to one, and a greater dominance factor implies that its corresponding operator will have more power in controlling the remote robot. The time-varying delays in the communication channels, the actuators saturation, and the nonlinear dynamics are incorporated into the controller development. Using Barbalat’s lemma and a Lyapunov-Krasovskii functional, the asymptotic stability analysis of the closed-loop dynamics are shown, and the performance claims are delivered. Simulation and experimental results are provided to verify the theoretical findings. },
keywords = {Cooperative control, Multilateral teleoperation, Saturation control, Task-space synchronization, Time-varying delay},
language = {English},
publisher = {Elsevier Ltd.}
}

In this article, a novel multilateral teleoperation structure is presented for cooperative control of a redundant remote robot in the task-space. In the structure, each local robot is assigned a real number between zero and one, as its dominance factor. The sum of all the assigned factors is equal to one, and a greater dominance factor implies that its corresponding operator will have more power in controlling the remote robot. The time-varying delays in the communication channels, the actuators saturation, and the nonlinear dynamics are incorporated into the controller development. Using Barbalat’s lemma and a Lyapunov-Krasovskii functional, the asymptotic stability analysis of the closed-loop dynamics are shown, and the performance claims are delivered. Simulation and experimental results are provided to verify the theoretical findings.

@article{blondin_2019,
title = {A holistic optimization approach for inverted cart-pendulum control tuning},
author = {Blondin, M.J.; Pardalos, P.M.},
journal = {Soft Computing},
year = {2019},
institution = {University of Florida, USA},
abstract = {The inverted cart-pendulum (ICP) is a nonlinear underactuated system, which dynamics are representative of many applications. Therefore, the development of ICP control laws is important since these laws are suitable to other systems. Indeed, many nonlinear control strategies have emerged from the control of the ICP. For these reasons, the ICP remains a canonical and fundamental benchmark problem in control theory and robotics that is of interest to the scientific community. Till now, the trial-and-error method is still widely applied for ICP controller tuning as well as the sequential tuning referring to tune the swing-up controller and thereafter, the stabilization controller. Therefore, the aim of this paper is to automate and facilitate the ICP control in one step. Thus, this paper proposes to holistically optimize ICP controllers. The holistic optimization is performed by a simplified Ant Colony Optimization method with a constrained Nelder–Mead algorithm (ACO-NM). Holistic optimization refers to a simultaneous tuning of the swing-up, stabilization and switching mode parameters. A new cost function is designed to minimize swing-up time, achieve high stabilization performance and consider system constraints. The holistic approach optimizes four controller structures, which include controllers that have never been tuned by a specific method besides by the trial-and-error method. Simulation results on a ICP nonlinear model show that ACO-NM in the holistic approach is effective compared to other algorithms. In addition, contrary to the majority of work on the subject, all the optimized controllers are validated experimentally. The simulation and experimental results obtained confirm that the holistic approach is an efficient optimization tool and specifically responds to the need of optimization technique for the potential-well controller structure and for the Q [diagonal of the matrix and the full matrix] in the linear–quadratic regulator (LQR) technique. Moreover, ICP experimental response analysis demonstrates that using the full Q provides greater experimental stabilization performance than using its diagonal terms in the LQR technique. },
issn = {1432-7643},
keywords = {Inverted cart-pendulum system, Nonlinear control, Optimization, Holistic approach, Swing-up, Stabilization },
language = {English},
publisher = {Springer Berlin Heidelberg}
}

The inverted cart-pendulum (ICP) is a nonlinear underactuated system, which dynamics are representative of many applications. Therefore, the development of ICP control laws is important since these laws are suitable to other systems. Indeed, many nonlinear control strategies have emerged from the control of the ICP. For these reasons, the ICP remains a canonical and fundamental benchmark problem in control theory and robotics that is of interest to the scientific community. Till now, the trial-and-error method is still widely applied for ICP controller tuning as well as the sequential tuning referring to tune the swing-up controller and thereafter, the stabilization controller. Therefore, the aim of this paper is to automate and facilitate the ICP control in one step. Thus, this paper proposes to holistically optimize ICP controllers. The holistic optimization is performed by a simplified Ant Colony Optimization method with a constrained Nelder–Mead algorithm (ACO-NM). Holistic optimization refers to a simultaneous tuning of the swing-up, stabilization and switching mode parameters. A new cost function is designed to minimize swing-up time, achieve high stabilization performance and consider system constraints. The holistic approach optimizes four controller structures, which include controllers that have never been tuned by a specific method besides by the trial-and-error method. Simulation results on a ICP nonlinear model show that ACO-NM in the holistic approach is effective compared to other algorithms. In addition, contrary to the majority of work on the subject, all the optimized controllers are validated experimentally. The simulation and experimental results obtained confirm that the holistic approach is an efficient optimization tool and specifically responds to the need of optimization technique for the potential-well controller structure and for the Q [diagonal of the matrix and the full matrix] in the linear–quadratic regulator (LQR) technique. Moreover, ICP experimental response analysis demonstrates that using the full Q provides greater experimental stabilization performance than using its diagonal terms in the LQR technique.

@article{alli_2019,
title = {A LabVIEW-based online DC servomechanism control experiments incorporating PID controller for students’ laboratory},
author = {Alli, K.S.},
journal = {The International Journal of Electrical Engineering & Education},
year = {2019},
institution = {The University of the West Indies, Jamaica},
abstract = {This research designed a DC motor control system experiments and developed a platform for on-line implementation of these experiments. This was with a view to contributing to the number of online laboratory experiments while providing a common platform for other on-line users. In this research, three experiments were designed using LabVIEW software. The developed platform is implemented online using the Massachusetts Institute of Technology interactive laboratory architecture. The control system performance was then evaluated by administering questionnaires to users with the aim of eliciting information on the ease of use of the operating system. The successful control of the DC motor and integration of the experiment engines into the iLab Interactive architecture were achieved. In conclusion, the online DC motor control laboratory was deployed and provided ready access to a DC motor control over the Internet with beginner and intermediate-level experiments in the field of control system and engineering. },
keywords = {LabVIEW, i-Lab, PID controller},
language = {English},
publisher = {SAGE}
}

This research designed a DC motor control system experiments and developed a platform for on-line implementation of these experiments. This was with a view to contributing to the number of online laboratory experiments while providing a common platform for other on-line users. In this research, three experiments were designed using LabVIEW software. The developed platform is implemented online using the Massachusetts Institute of Technology interactive laboratory architecture. The control system performance was then evaluated by administering questionnaires to users with the aim of eliciting information on the ease of use of the operating system. The successful control of the DC motor and integration of the experiment engines into the iLab Interactive architecture were achieved. In conclusion, the online DC motor control laboratory was deployed and provided ready access to a DC motor control over the Internet with beginner and intermediate-level experiments in the field of control system and engineering.

@conference{sousa_2019,
title = {A Learning Controller Design Approach for a 3-DOF Helicopter System with Online Optimal Control},
author = {Sousa, G.B.; Lima, J.V.R.; Rego, P.H.M.; Souza, A.G.; Fonseca Neto, J.V.},
year = {2019},
institution = {State University of Maranhão, Brazil; Technological Institute of Aeronautics, Brazil; Federal University of Maranhão, Brazil},
abstract = {This paper presents the design and investigation of performance of a 3-DOF Quanser helicopter system using a learning optimal control approach that is grounded on approximate dynamic programming paradigms, specifically action-dependent heuristic dynamic programming (ADHDP). This approach results in an algorithm that is embedded in the actor-critic reinforcement learning architecture, that characterizes this design as a model-free structure. The developed methodology aims at implementing an optimal controller that acts in real-time in the plant control, using only the input and output signals and states measured along the system trajectories. The feedback control design technique is capable of an online tuning of the controller parameters according to the plant dynamics, which is subject to the model uncertainties and external disturbances. The experimental results demonstrate the desired performance of the proposed controller implemented on the 3-DOF Quanser helicopter. },
keywords = {Action-Dependent Heuristic Dynamic Programming, Actor-Critic Reinforcement Learning, Real-Time Control, 3-DOF Helicopter},
language = {English}
}

This paper presents the design and investigation of performance of a 3-DOF Quanser helicopter system using a learning optimal control approach that is grounded on approximate dynamic programming paradigms, specifically action-dependent heuristic dynamic programming (ADHDP). This approach results in an algorithm that is embedded in the actor-critic reinforcement learning architecture, that characterizes this design as a model-free structure. The developed methodology aims at implementing an optimal controller that acts in real-time in the plant control, using only the input and output signals and states measured along the system trajectories. The feedback control design technique is capable of an online tuning of the controller parameters according to the plant dynamics, which is subject to the model uncertainties and external disturbances. The experimental results demonstrate the desired performance of the proposed controller implemented on the 3-DOF Quanser helicopter.

@article{bernstein_2019,
title = {A Moving Horizon State Estimator for Real-Time Stabilization of a Double Inverted Pendulum},
author = {Bernstein, A.; King, E.; Tran, H.},
year = {2019},
institution = {North Carolina State University, USA},
abstract = {A moving horizon estimator is designed in the framework of the proximal point minimization algorithm for linear time invariant systems and convergence results are established in the presence of model and measurement noise. The state estimator is implemented in a nonlinear suboptimal feedback control framework for the real time stabilization of a double inverted pendulum on a cart. },
language = {English}
}

A moving horizon estimator is designed in the framework of the proximal point minimization algorithm for linear time invariant systems and convergence results are established in the presence of model and measurement noise. The state estimator is implemented in a nonlinear suboptimal feedback control framework for the real time stabilization of a double inverted pendulum on a cart.

@article{cheng_2019,
title = {A multilateral impedance-controlled system for haptics-enabled surgical training and cooperation in beating-heart surgery},
author = {Cheng, L.; Tavakoli, M.},
journal = {International Journal of Intelligent Robotics and Applications},
year = {2019},
institution = {University of Alberta, Canada},
abstract = {In this paper, an impedance-controlled multi-master/single-slave telerobotic system is developed for haptics-enabled surgical training and cooperation in beating-heart surgery. This system not only can enable automatically motion compensation for the beating heart’s motion as well as non-oscillatory force feedback to the human operators but can also enable training and cooperation for multiple users. A multi-user shared control architecture is developed, and a multilateral impedance-controlled strategy is employed for this architecture. The desired objectives of the proposed system are (a) providing position guidance to the trainees during training procedure, (b) providing force feedback to all human operators (trainer and trainees) regardless of their levels of authority over the slave robot, (c) motion compensation for the heart’s motion, and (d) reflecting only the non-oscillatory force portion of the slave-heart tissue interaction force to all human operators. To this end, virtual fixtures and a dominance factor are introduced, and a reference impedance model with adjusted parameters is designed for each master or slave robot. The proposed impedance-based control methodology is evaluated experimentally. The experimental results demonstrated that the proposed method could be used for surgical training and cooperation in beating-heart surgery by providing appropriate position guidance and environmental force feedback to the human operators. },
issn = {2366-5971},
keywords = {Motion compensation, Haptic feedback, Impedance model, Teleoperation system, Medical robots },
language = {English},
publisher = {Springer, Singapore}
}

In this paper, an impedance-controlled multi-master/single-slave telerobotic system is developed for haptics-enabled surgical training and cooperation in beating-heart surgery. This system not only can enable automatically motion compensation for the beating heart’s motion as well as non-oscillatory force feedback to the human operators but can also enable training and cooperation for multiple users. A multi-user shared control architecture is developed, and a multilateral impedance-controlled strategy is employed for this architecture. The desired objectives of the proposed system are (a) providing position guidance to the trainees during training procedure, (b) providing force feedback to all human operators (trainer and trainees) regardless of their levels of authority over the slave robot, (c) motion compensation for the heart’s motion, and (d) reflecting only the non-oscillatory force portion of the slave-heart tissue interaction force to all human operators. To this end, virtual fixtures and a dominance factor are introduced, and a reference impedance model with adjusted parameters is designed for each master or slave robot. The proposed impedance-based control methodology is evaluated experimentally. The experimental results demonstrated that the proposed method could be used for surgical training and cooperation in beating-heart surgery by providing appropriate position guidance and environmental force feedback to the human operators.

@article{sajjadi_2019,
title = {A Nonlinear Adaptive Model-Predictive Approach for Visual Servoing of Unmanned Aerial Vehicles},
author = {Sajjadi, S.; Mehrandezh, M.; Janabi-Sharifi, F.},
booktitle = {Progress in Optomechatronic Technologies},
year = {2019},
institution = {University of Regina, Canada; Ryerson University, Canada},
abstract = {Aerial vehicles, due to the nature of their operations, are subject to many uncertainties. In particular, their positioning control problem is aggravated in GPS-denied environments. As a result, vision-based control (or visual servoing) solutions are sought to address their navigation control issue. In this work, visual servoing is formulated as a nonlinear optimization problem in the image plane. The proposed approach is based on a class of nonlinear model-predictive control that takes constraints into consideration. A 2-DOF model helicopter is considered for initial formulations and verification of the approach. The considered model helicopter has a coupled nonlinear pitch/yaw dynamics. It is also capable of a pitch over (i.e., hover at a non-zero pitch angle) around its pivotal axis of rotation due to the offset between the center of mass (CoM) of its fuselage and the rotation axis. This further makes the linearized model (calculated around any pitch angle). A linear parameter varying (LPV) system for an adaptive control is formulated under a model-predictive visual servoing framework. In this paper, a design guideline is provided under a model-predictive visual servoing paradigm that considers field of view (FOV) constraints, the constraint on the pitch angle, and also the maximum voltages that can be applied to independently controlled pitch/yaw motors. The control space is parameterized via Laguerre basis network functions that make the optimization scheme computationally less expensive, thus suitable for real-time applications. },
language = {English},
series = {Springer Proceedings in Physics},
publisher = {Springer, Singapore},
isbn = {978-981-32-9631-2},
pages = {153-164}
}

Aerial vehicles, due to the nature of their operations, are subject to many uncertainties. In particular, their positioning control problem is aggravated in GPS-denied environments. As a result, vision-based control (or visual servoing) solutions are sought to address their navigation control issue. In this work, visual servoing is formulated as a nonlinear optimization problem in the image plane. The proposed approach is based on a class of nonlinear model-predictive control that takes constraints into consideration. A 2-DOF model helicopter is considered for initial formulations and verification of the approach. The considered model helicopter has a coupled nonlinear pitch/yaw dynamics. It is also capable of a pitch over (i.e., hover at a non-zero pitch angle) around its pivotal axis of rotation due to the offset between the center of mass (CoM) of its fuselage and the rotation axis. This further makes the linearized model (calculated around any pitch angle). A linear parameter varying (LPV) system for an adaptive control is formulated under a model-predictive visual servoing framework. In this paper, a design guideline is provided under a model-predictive visual servoing paradigm that considers field of view (FOV) constraints, the constraint on the pitch angle, and also the maximum voltages that can be applied to independently controlled pitch/yaw motors. The control space is parameterized via Laguerre basis network functions that make the optimization scheme computationally less expensive, thus suitable for real-time applications.

@article{butt_2019,
title = {A Nonlinear Integral Sliding Surface to Improve the Transient Response of a Force-Controlled Pneumatic Actuator With Long Transmission Lines},
author = {Butt, K.; Sepehri, N.},
journal = {Journal of Dynamic Systems, Measurement, and Control},
year = {2019},
month = {12},
volume = {141},
number = {12},
institution = {University of Manitoba, Canada},
abstract = {A force-controlled pneumatic actuator with long connecting tubes is a well-accepted solution to develop magnetic resonance imaging (MRI)-compatible force control applications. Such an actuator represents an uncertain, second-order, nonlinear system with input delay. The integral sliding mode control, because of guaranteed robustness against matched uncertainties throughout the system response, provides a favorable option to design a robust controller for the actuator. However, if the controller is based on a linear integral sliding surface (LISS), the response of the actuator overshoots, especially when there are large initial errors. Minimizing overshoot results in a smaller controller bandwidth and a slower system response. This paper presents a novel nonlinear integral sliding surface (NLISS) for a sliding mode controller to improve the transient response of the actuator. The proposed surface is a LISS augmented by a nonlinear function of tracking error and does not have a reaching phase when there are initial errors and even multiple steps in the desired trajectory. The surface enables the integral sliding mode controller to offer variable damping, which changes from low to high as the transient error approaches small values and vice versa. Simulation studies and experimental results show that the controller based on the proposed sliding surface successfully eliminates the overshoot without compromising the controller bandwidth, rise, and settling times. For performance evaluation, the controller parameters are tuned using the globalized and bounded Nelder–Mead (GBNM) algorithm with deterministic restarts. The study also establishes the asymptotic stability of the controller based on the proposed sliding surface using Lyapunov's stability criterion. },
keywords = {force control, pneumatic actuator, input delay, integral sliding mode control, nonlinear integral sliding surface},
language = {English},
publisher = {ASME}
}

A force-controlled pneumatic actuator with long connecting tubes is a well-accepted solution to develop magnetic resonance imaging (MRI)-compatible force control applications. Such an actuator represents an uncertain, second-order, nonlinear system with input delay. The integral sliding mode control, because of guaranteed robustness against matched uncertainties throughout the system response, provides a favorable option to design a robust controller for the actuator. However, if the controller is based on a linear integral sliding surface (LISS), the response of the actuator overshoots, especially when there are large initial errors. Minimizing overshoot results in a smaller controller bandwidth and a slower system response. This paper presents a novel nonlinear integral sliding surface (NLISS) for a sliding mode controller to improve the transient response of the actuator. The proposed surface is a LISS augmented by a nonlinear function of tracking error and does not have a reaching phase when there are initial errors and even multiple steps in the desired trajectory. The surface enables the integral sliding mode controller to offer variable damping, which changes from low to high as the transient error approaches small values and vice versa. Simulation studies and experimental results show that the controller based on the proposed sliding surface successfully eliminates the overshoot without compromising the controller bandwidth, rise, and settling times. For performance evaluation, the controller parameters are tuned using the globalized and bounded Nelder–Mead (GBNM) algorithm with deterministic restarts. The study also establishes the asymptotic stability of the controller based on the proposed sliding surface using Lyapunov's stability criterion.

@article{harmanci_2019,
title = {A Novel Approach for 3D-Structural Identification through Video Recording: Magnified Tracking},
author = {Harmanci, Y.E.; Gülan, U.; Holzner, M.; Chatzi, E. },
journal = {Sensors},
year = {2019},
volume = {19},
number = {5},
institution = {ETH Zürich, Switzerland},
abstract = {Advancements in optical imaging devices and computer vision algorithms allow the exploration of novel diagnostic techniques for use within engineering systems. A recent field of application lies in the adoption of such devices for non-contact vibrational response recordings of structures, allowing high spatial density measurements without the burden of heavy cabling associated with conventional technologies. This, however, is not a straightforward task due to the typically low-amplitude displacement response of structures under ambient operational conditions. A novel framework, namely Magnified Tracking (MT), is proposed herein to overcome this limitation through the synergistic use of two computer vision techniques. The recently proposed phase-based motion magnification (PBMM) framework, for amplifying motion in a video within a defined frequency band, is coupled with motion tracking by means of particle tracking velocimetry (PTV). An experimental campaign was conducted to validate a proof-of-concept, where the dynamic response of a shear frame was measured both by conventional sensors as well as a video camera setup, and cross-compared to prove the feasibility of the proposed non-contact approach. The methodology was explored both in 2D and 3D configurations, with PTV revealing a powerful tool for the measurement of perceptible motion. When MT is utilized for tracking “imperceptible” structural responses (i.e., below PTV sensitivity), via the use of PBMM around the resonant frequencies of the structure, the amplified motion reveals the operational deflection shapes, which are otherwise intractable. The modal results extracted from the magnified videos, using PTV, demonstrate MT to be a viable non-contact alternative for 3D modal identification with the benefit of a spatially dense measurement grid. },
keywords = {vibration-based measurement; SHM; structural identification; motion magnification; particle tracking velocimetry},
language = {English},
publisher = {MDPI}
}

Advancements in optical imaging devices and computer vision algorithms allow the exploration of novel diagnostic techniques for use within engineering systems. A recent field of application lies in the adoption of such devices for non-contact vibrational response recordings of structures, allowing high spatial density measurements without the burden of heavy cabling associated with conventional technologies. This, however, is not a straightforward task due to the typically low-amplitude displacement response of structures under ambient operational conditions. A novel framework, namely Magnified Tracking (MT), is proposed herein to overcome this limitation through the synergistic use of two computer vision techniques. The recently proposed phase-based motion magnification (PBMM) framework, for amplifying motion in a video within a defined frequency band, is coupled with motion tracking by means of particle tracking velocimetry (PTV). An experimental campaign was conducted to validate a proof-of-concept, where the dynamic response of a shear frame was measured both by conventional sensors as well as a video camera setup, and cross-compared to prove the feasibility of the proposed non-contact approach. The methodology was explored both in 2D and 3D configurations, with PTV revealing a powerful tool for the measurement of perceptible motion. When MT is utilized for tracking “imperceptible” structural responses (i.e., below PTV sensitivity), via the use of PBMM around the resonant frequencies of the structure, the amplified motion reveals the operational deflection shapes, which are otherwise intractable. The modal results extracted from the magnified videos, using PTV, demonstrate MT to be a viable non-contact alternative for 3D modal identification with the benefit of a spatially dense measurement grid.

@article{perez-alcocer_2019,
title = {A novel Lyapunov-based trajectory tracking controller for a quadrotor: Experimental analysis by using two motion tasks},
author = {Perez-Alcocer, R.; Moreno-Valenzula, J.},
journal = {Mechatronics},
year = {2019},
month = {08},
volume = {61},
institution = {Instituto Politécnico Nacional-CITEDI, Mexico},
abstract = {A novel model-based controller for quadrotor trajectory tracking is presented in this paper. The closed-loop stability is studied by Lyapunov’s theory guaranteeing local asymptotical stability of the resulting equilibrium point. The performance of the proposed scheme is compared in real time with respect to three known trajectory tracking controllers having different structures. Besides, two desired trajectories encoding different motion tasks are specified. The gains of the tested controllers are selected so that the mean value of the total thrust is the same. An analysis by using different performance indexes is employed in order assess the tracking performance of each scheme. The proposed controller presents the best experimental execution for the two implemented motion tasks. },
keywords = {Quadrotor, Trajectory tracking, Model-based controller, Lyapunov theory, Real–time experiments},
language = {English},
publisher = {Elsevier Ltd.}
}

A novel model-based controller for quadrotor trajectory tracking is presented in this paper. The closed-loop stability is studied by Lyapunov’s theory guaranteeing local asymptotical stability of the resulting equilibrium point. The performance of the proposed scheme is compared in real time with respect to three known trajectory tracking controllers having different structures. Besides, two desired trajectories encoding different motion tasks are specified. The gains of the tested controllers are selected so that the mean value of the total thrust is the same. An analysis by using different performance indexes is employed in order assess the tracking performance of each scheme. The proposed controller presents the best experimental execution for the two implemented motion tasks.

@article{huang_2019,
title = {A PSO-tuned Fuzzy Logic System for Position Tracking of Mobile Robot},
author = {Huang, C.; Farooq, U.; Liu, H.; Gu, J.; Luo, J.},
journal = {International Journal of Robotics and Automation},
year = {2019},
volume = {206},
abstract = {In this paper, we proposed a simple but practical fuzzy logic position tracking controller for a wheeled mobile robot. Certain mobile robot with symmetric or ignorable body shape may focus on its position precision on a path tracking task. In this case, we assigned its position errors as input of our fuzzy logic controller and its linear and angular velocities as outputs. Then, we optimized it by tuning the parameters of scaling and membership functions using a particle swarm optimization. Our fuzzy logic system was optimized and simulated in Matlab and Simulink and was experimentally tested on Quanser’s Qbot2 robot with Quarc control system. After optimization, the values of Fitness Function (cumulative position errors) have distinctly decreased from 35.9642 to 5.8246 in simulation and from 37.4041 to 7.3935 experimentally. These results had shown that the optimized system outperformed the same system without optimization. },
language = {English},
publisher = {Acta Press}
}

In this paper, we proposed a simple but practical fuzzy logic position tracking controller for a wheeled mobile robot. Certain mobile robot with symmetric or ignorable body shape may focus on its position precision on a path tracking task. In this case, we assigned its position errors as input of our fuzzy logic controller and its linear and angular velocities as outputs. Then, we optimized it by tuning the parameters of scaling and membership functions using a particle swarm optimization. Our fuzzy logic system was optimized and simulated in Matlab and Simulink and was experimentally tested on Quanser’s Qbot2 robot with Quarc control system. After optimization, the values of Fitness Function (cumulative position errors) have distinctly decreased from 35.9642 to 5.8246 in simulation and from 37.4041 to 7.3935 experimentally. These results had shown that the optimized system outperformed the same system without optimization.

@article{qin_2019,
title = {A research of sliding mode control method with disturbance observer combining skyhook model for active suspension systems},
author = {Qin, W.; Shangguan, W.-B.; Zhao, K.},
journal = {Journal of Vibration and Control},
year = {2019},
institution = {South China University of Technology, China},
abstract = {Based on a nonlinear two-degree-of-freedom model of active suspension systems, an approach of the sliding mode control with disturbance observer combining skyhook model sliding mode control with disturbance observer combining is proposed for improving the performance of active suspension systems, and the effectiveness of the proposed approach is validated by the active suspension system plant. Two problems of active suspension systems are solved by using the proposed approach when the tire is excited by the step displacement. One problem is that the suspension deflection of active suspension systems, i.e. the difference between the sprung mass displacement and the unsprung mass displacement, using conventional sliding mode control with disturbance observer not converges to zero in finite time, and the phenomenon of the impact of suspension against the limit block is produced. This problem is solved by providing a reference value of the sprung mass displacement in an active suspension system, which is obtained from the skyhook model. The other problem is that disturbances exist in active suspension systems, which are caused by the inaccurate parameters of stiffness and damping. This problem is solved by designing a disturbance observer to estimate the summation of the disturbances. Finally, the performance indexes of the active suspension system with the sliding mode control with disturbance observer combining skyhook model are calculated and compared with those of using the conventional sliding mode control with disturbance observer and the linear quadratic regulator approach. },
keywords = {Nonlinear two-degree-of-freedom model, sliding mode control with disturbance observer, suspension deflection, performance indexes},
language = {English},
publisher = {SAGE Publications}
}

Based on a nonlinear two-degree-of-freedom model of active suspension systems, an approach of the sliding mode control with disturbance observer combining skyhook model sliding mode control with disturbance observer combining is proposed for improving the performance of active suspension systems, and the effectiveness of the proposed approach is validated by the active suspension system plant. Two problems of active suspension systems are solved by using the proposed approach when the tire is excited by the step displacement. One problem is that the suspension deflection of active suspension systems, i.e. the difference between the sprung mass displacement and the unsprung mass displacement, using conventional sliding mode control with disturbance observer not converges to zero in finite time, and the phenomenon of the impact of suspension against the limit block is produced. This problem is solved by providing a reference value of the sprung mass displacement in an active suspension system, which is obtained from the skyhook model. The other problem is that disturbances exist in active suspension systems, which are caused by the inaccurate parameters of stiffness and damping. This problem is solved by designing a disturbance observer to estimate the summation of the disturbances. Finally, the performance indexes of the active suspension system with the sliding mode control with disturbance observer combining skyhook model are calculated and compared with those of using the conventional sliding mode control with disturbance observer and the linear quadratic regulator approach.

@conference{erwin_2019,
title = {A Robotic Platform for 3D Forelimb Rehabilitation with Rats},
author = {Erwin, A.; Gallegos, C.; Cao, Q.; O’Malley, M.K. },
booktitle = {2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR)},
year = {2019},
institution = {Universities Space Research Association, USA; The University of Texas Health Science Center, USA; Rice University, USA},
abstract = { In an attempt to promote greater functional recovery after spinal cord injury, researchers have begun exploring combinatorial treatments, such as robotic rehabilitation combined with stem cell transplantation. Since these treatment methods are in their nascent stages, rodent models have been proposed for initial investigations. Robots have been built for locomotion rehabilitation and planar forelimb reach and grasp assessment with rodents; however, a robotic platform suitable for three-dimensional movement rehabilitation of the rodent forelimb has not yet been developed. In this paper, a novel three degree of freedom robotic manipulator for automated forelimb rehabilitation combined with stem cell transplantation after cervical spinal cord injury with rats is proposed. The robot interfaces with a rat in an end-effector manner, measuring and interacting with the forelimb in the 3D Cartesian space. In this work, we trained two rats through behavioral shaping to actively interact with the device during two robot control modes. This work provides preliminary investigations into the feasibility of 3D forelimb rehabilitation with rats, which could be translated as a paradigm for combinatorial treatments after spinal cord injury in a controlled manner. },
issn = {1945-7898},
keywords = {Rats, Training, Wrist, Three-dimensional displays, End effectors, Robot kinematics},
language = {English},
publisher = {IEEE},
isbn = {978-1-7281-2756-9 }
}

In an attempt to promote greater functional recovery after spinal cord injury, researchers have begun exploring combinatorial treatments, such as robotic rehabilitation combined with stem cell transplantation. Since these treatment methods are in their nascent stages, rodent models have been proposed for initial investigations. Robots have been built for locomotion rehabilitation and planar forelimb reach and grasp assessment with rodents; however, a robotic platform suitable for three-dimensional movement rehabilitation of the rodent forelimb has not yet been developed. In this paper, a novel three degree of freedom robotic manipulator for automated forelimb rehabilitation combined with stem cell transplantation after cervical spinal cord injury with rats is proposed. The robot interfaces with a rat in an end-effector manner, measuring and interacting with the forelimb in the 3D Cartesian space. In this work, we trained two rats through behavioral shaping to actively interact with the device during two robot control modes. This work provides preliminary investigations into the feasibility of 3D forelimb rehabilitation with rats, which could be translated as a paradigm for combinatorial treatments after spinal cord injury in a controlled manner.

@article{arabi_2019,
title = {A set-theoretic model reference adaptive control architecture with dead-zone effect},
author = {Arabi, E.; Yucelen, T.},
journal = {Control Engineering Practice},
year = {2019},
month = {08},
volume = {89},
institution = {University of Michigan, USA; University of South Florida, USA},
abstract = {A new set-theoretic model reference adaptive control architecture with dead-zone effect is presented. The key feature of our approach utilizes a new generalized restricted potential function, where it not only provides a user-defined uncertain dynamical system performance but also has the capability to stop the adaptation when system errors are small (i.e., inside dead-zone) — a practice adopted in adaptive control applications. The stability of the proposed technique is analyzed through showing the boundedness of an energy function in all possible variations and its experimental validation is also given through an aerospace testbed. },
keywords = {Uncertain dynamical systems, Set-theoretic model reference adaptive control, Dead-zone effect, User-defined worst-case performance guarantees, Stability analysis Experiments},
language = {English},
publisher = {Elsevier Ltd.},
pages = {12-29}
}

A new set-theoretic model reference adaptive control architecture with dead-zone effect is presented. The key feature of our approach utilizes a new generalized restricted potential function, where it not only provides a user-defined uncertain dynamical system performance but also has the capability to stop the adaptation when system errors are small (i.e., inside dead-zone) — a practice adopted in adaptive control applications. The stability of the proposed technique is analyzed through showing the boundedness of an energy function in all possible variations and its experimental validation is also given through an aerospace testbed.

@conference{kutuk_2019,
title = {A Simulation Tool for Kinematics Analysis of a Serial Robot},
author = {Kutuk, M.E.; Dulger, L.C.; Das, M.T.},
booktitle = {DSMIE 2019: Advances in Design, Simulation and Manufacturing },
year = {2019},
institution = {Gaziantep University, Turkey; Izmir University of Economics, Turkey; Kirrikale University, Turkey},
abstract = {Robot programming is a very significant task in the field of robotics. Off-line programming (OLP) is a method performed before robot manipulation. It is the manual editing of the robot code using computer software to simulate the real robotic scenarios. Task sequence planning, short-term production, flexibility during operation and expecting real behaviour of the robots are some of the reasons that make the users prefer OLP. Operations can be visualized in many processes such as welding, cutting, even medical applications. In this study, off-line models are offered including the forward and inverse kinematics of a six Degree-Of-Freedom (DOF) serial robot manipulator (Denso VP-6242G). Robotic Toolbox combined with GUI Development Environment in Matlab® is used for the forward kinematics solution. A Matlab® Simulink model with Simmechanics blocks is used in the inverse kinematic analysis. Visualization is enriched by 3D Solidworks® models of the robot parts. Basic motion examples that can be used in many areas are presented. },
keywords = {Off-line programming (OLP), Denso VP-6242G, Forward and inverse kinematics, Robotic toolbox },
language = {English},
publisher = {Springe, Cham},
isbn = {978-3-030-22364-9}
}

Robot programming is a very significant task in the field of robotics. Off-line programming (OLP) is a method performed before robot manipulation. It is the manual editing of the robot code using computer software to simulate the real robotic scenarios. Task sequence planning, short-term production, flexibility during operation and expecting real behaviour of the robots are some of the reasons that make the users prefer OLP. Operations can be visualized in many processes such as welding, cutting, even medical applications. In this study, off-line models are offered including the forward and inverse kinematics of a six Degree-Of-Freedom (DOF) serial robot manipulator (Denso VP-6242G). Robotic Toolbox combined with GUI Development Environment in Matlab® is used for the forward kinematics solution. A Matlab® Simulink model with Simmechanics blocks is used in the inverse kinematic analysis. Visualization is enriched by 3D Solidworks® models of the robot parts. Basic motion examples that can be used in many areas are presented.

@article{chan_2019,
title = {A smart mechatronic base isolation system using earthquake early warning},
author = {Chan, R.W.K.; Lin, Y.-S.; Tagawa, H.},
journal = {Soil Dynamics and Earthquake Engineering},
year = {2019},
month = {04},
volume = {119},
institution = {RMIT University, Australia; Hiroshima University, Japan},
abstract = {In earthquake-prone countries such as Japan, effects of earthquakes are one of the major design objectives in buildings and infrastructures. Recent advances in earthquake engineering have improved the safety and reliability of buildings and infrastructures. Better understanding of geomorphology of earthquakes also enables us to better predict and estimate propagation of such natural hazard. For example, the Earthquake Early Warning (EEW) system in Japan provides advanced warnings using the different arrival times of P and S waves. On the other hand, techniques and methods in passive, semi-active and active vibration control have flourished. Base-isolation is a proven technique which enhances earthquake resilience of buildings and bridges. In general, this technique involves decoupling a superstructure from its foundation and lengthens its natural period of vibration. This technique is mature and commercialization has taken place worldwide. However, wind-resistance of base-isolated structures is generally a concern as the lateral stiffness of a base-isolated structure is low and serviceability wind effects may induce unacceptable lateral movement and/or vibration of the structure. This paper presents a new concept which the base-isolation system is activated by EEW system through a mechatronic system. In other times the structure is not base-isolated and provides strong lateral resistance against wind loads. As a backup design, the proposed system also equips with its own network of accelerometers which can trigger base isolation system independent from EEW system. The smart mechatronic base isolation system is fully automated, and resets itself after each ground motion. The paper describes the conceptual framework of proposed system and presents laboratory-scaled proof-of-concept experiments. Four historical earthquake time histories were used as ground excitations. Structural responses are compared between the two triggering mechanisms and results are discussed. Finally, the concept of Internet of Things (IoT) which make use of EEW is discussed, allowing connectivity between a single control unit and a network of infrastructures to achieve economy of scale. },
keywords = {Smart structures, Base isolation, Earthquake early warning, Internet of things},
language = {English},
publisher = {Elsevier Ltd.},
pages = {299-307}
}

In earthquake-prone countries such as Japan, effects of earthquakes are one of the major design objectives in buildings and infrastructures. Recent advances in earthquake engineering have improved the safety and reliability of buildings and infrastructures. Better understanding of geomorphology of earthquakes also enables us to better predict and estimate propagation of such natural hazard. For example, the Earthquake Early Warning (EEW) system in Japan provides advanced warnings using the different arrival times of P and S waves. On the other hand, techniques and methods in passive, semi-active and active vibration control have flourished. Base-isolation is a proven technique which enhances earthquake resilience of buildings and bridges. In general, this technique involves decoupling a superstructure from its foundation and lengthens its natural period of vibration. This technique is mature and commercialization has taken place worldwide. However, wind-resistance of base-isolated structures is generally a concern as the lateral stiffness of a base-isolated structure is low and serviceability wind effects may induce unacceptable lateral movement and/or vibration of the structure. This paper presents a new concept which the base-isolation system is activated by EEW system through a mechatronic system. In other times the structure is not base-isolated and provides strong lateral resistance against wind loads. As a backup design, the proposed system also equips with its own network of accelerometers which can trigger base isolation system independent from EEW system. The smart mechatronic base isolation system is fully automated, and resets itself after each ground motion. The paper describes the conceptual framework of proposed system and presents laboratory-scaled proof-of-concept experiments. Four historical earthquake time histories were used as ground excitations. Structural responses are compared between the two triggering mechanisms and results are discussed. Finally, the concept of Internet of Things (IoT) which make use of EEW is discussed, allowing connectivity between a single control unit and a network of infrastructures to achieve economy of scale.

@article{jeon_2019,
title = {A Stealthy Sensor Attack for Uncertain Cyber-Physical Systems},
author = {Jeon, H.; Eu, Y.},
journal = {Internet of Things Journal},
year = {2019},
institution = {DGIST, South Korea},
abstract = { In this paper, we present a sensor attack on Cyber-Physical Systems (CPS), which can be constructed with limited knowledge of the target system and can remain stealthy until the attack succeeds. The target CPS consists of a physical plant with unstable linear dynamics and a feedback controller. Specifically, the attack mechanism is to impede the stabilizing function of the feedback controller by injecting false data to the sensors where the false data are created using the unstable dynamics of the plant. When the only nominal model for the target dynamics is known, the stealthiness is maintained by deploying a mechanism similar to a disturbance observer which can be designed to absorb the effect of the mismatch between the nominal and actual dynamics until the attack succeeds. The success of the attack is defined by the norm of the system state exceeding a threshold. Sensor attacks that exploit unstable plant dynamics had been conceived previously. Generation of such attacks require precise knowledge of the target system for stealthiness, i.e., the attack must cancel at the sensor exactly the effect of instability in order to avoid detection. When not exact, the mismatch grows exponentially leading to the detection of abnormality. The attack presented in this work absorbs the mismatch using the disturbance observer mechanism, where the degree of absorption is selected such that the detection is delayed until the attack succeeds. Thus, the proposed attack, compared to the conventional ones, poses a greater level of threat to CPS. In this work, generation of the attack is presented, and the effect is analyzed. The consequence of the attack is illustrated and emphasized by simulations on quadrotors and inverted pendulums, respectively. },
issn = {2327-4662 },
keywords = {Cyber-physical systems, stealthy sensor attack, Model uncertainty},
language = {English},
publisher = {IEEE}
}

In this paper, we present a sensor attack on Cyber-Physical Systems (CPS), which can be constructed with limited knowledge of the target system and can remain stealthy until the attack succeeds. The target CPS consists of a physical plant with unstable linear dynamics and a feedback controller. Specifically, the attack mechanism is to impede the stabilizing function of the feedback controller by injecting false data to the sensors where the false data are created using the unstable dynamics of the plant. When the only nominal model for the target dynamics is known, the stealthiness is maintained by deploying a mechanism similar to a disturbance observer which can be designed to absorb the effect of the mismatch between the nominal and actual dynamics until the attack succeeds. The success of the attack is defined by the norm of the system state exceeding a threshold. Sensor attacks that exploit unstable plant dynamics had been conceived previously. Generation of such attacks require precise knowledge of the target system for stealthiness, i.e., the attack must cancel at the sensor exactly the effect of instability in order to avoid detection. When not exact, the mismatch grows exponentially leading to the detection of abnormality. The attack presented in this work absorbs the mismatch using the disturbance observer mechanism, where the degree of absorption is selected such that the detection is delayed until the attack succeeds. Thus, the proposed attack, compared to the conventional ones, poses a greater level of threat to CPS. In this work, generation of the attack is presented, and the effect is analyzed. The consequence of the attack is illustrated and emphasized by simulations on quadrotors and inverted pendulums, respectively.

@article{cajo_2019,
title = {A Survey on Fractional Order Control Techniques for Unmanned Aerial and Ground Vehicles},
author = {Cajo, R.; Mac, T.T.; Plaza, D.; Copot, C.; De Keyser, R.; Ionescu, C.},
journal = {IEEE Access},
year = {2019},
volume = {7},
institution = {Ghent University, Belgium; Hanoi University of Science and Technology, Vietnam; Escuela Superior Politécnica del Litoral, Ecuador; University of Antwerp, Belgium},
abstract = {In recent years, numerous applications of science and engineering for modeling and control of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) systems based on fractional calculus have been realized. The extra fractional order derivative terms allow to optimizing the performance of the systems. The review presented in this paper focuses on the control problems of the UAVs and UGVs that have been addressed by the fractional order techniques over the last decade. },
issn = {2169-3536},
keywords = {Fractional calculus, fractional order control techniques, unmanned aerial vehicles (UAVs), unmanned ground vehicles (UGVs)},
language = {English},
publisher = {IEEE},
pages = { 66864 - 66878}
}

In recent years, numerous applications of science and engineering for modeling and control of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) systems based on fractional calculus have been realized. The extra fractional order derivative terms allow to optimizing the performance of the systems. The review presented in this paper focuses on the control problems of the UAVs and UGVs that have been addressed by the fractional order techniques over the last decade.

@article{rodriguez-cortes_2019,
title = {A swinging up controller for the Furuta pendulum based on the Total Energy Control System approach},
author = {Rodriguez-Cortes, H.},
journal = {Kybernetika},
year = {2019},
volume = {55},
number = {2},
institution = {CINVESTAV-IPN, Mexico},
abstract = {This paper considers the problem of swinging up the Furuta pendulum and proposes a new smooth nonlinear swing up controller based on the concept of energy. This new controller results from the Total Energy Control System (TECS) approach in conjunction with a linearizing feedback controller. The new controller commands to the desired reference the total energy rate of the Furuta pendulum; thus, the Furuta pendulum oscillates and reaches a neighborhood of its unstable configuration while the rotation of its base remains bounded. Once the Furuta pendulum configuration is in the neighborhood of its unstable equilibrium point, a linear controller stabilizes the unstable configuration of the Furuta pendulum. Real-time experiments are included to support the theoretical developments. },
keywords = {total energy control system, Furuta pendulum, swinging up control, real-time experiments},
language = {English},
publisher = {Institute of Information Theory and Automation AS CR},
pages = {402-421}
}

This paper considers the problem of swinging up the Furuta pendulum and proposes a new smooth nonlinear swing up controller based on the concept of energy. This new controller results from the Total Energy Control System (TECS) approach in conjunction with a linearizing feedback controller. The new controller commands to the desired reference the total energy rate of the Furuta pendulum; thus, the Furuta pendulum oscillates and reaches a neighborhood of its unstable configuration while the rotation of its base remains bounded. Once the Furuta pendulum configuration is in the neighborhood of its unstable equilibrium point, a linear controller
stabilizes the unstable configuration of the Furuta pendulum. Real-time experiments are included to support the theoretical developments.

@conference{al-younes_2019,
title = {Actuator Fault-Diagnosis and Fault-Tolerant-Control using intelligent-Output-Estimator Applied on Quadrotor UAV},
author = {Al Younes, Y.; Noura, H.; Rabhi, A.; El Hajjaji, A.},
booktitle = {2019 International Conference on Unmanned Aircraft Systems (ICUAS)},
year = {2019},
institution = {Higher Colleges of Technology, UAE; Islamic University of Lebanon, Lebanon; University of Picardie, France},
abstract = {Actuator fault is a prominent system’s issue that attracts the researchers’ attentions. One type of actuator fault is a constant Loss-of-Effectiveness (LoE). In this paper, two systematic algorithms are presented. The first algorithm is designed to detect and diagnosis the actuator fault. This Fault-Detection-and-Diagnosis (FDD) approach relies on an estimator design that is called intelligent Output-Estimator (iOE). The proposed iOE is intended to improve the estimation of the actuator fault based on the Model-Free technique. The second algorithm works online after detecting, isolating and estimating the actuator fault. This Active-Fault-Tolerant-Control (AFTC) algorithm will use the estimated actuator value in a reconfigured control design to compensate for the fault. Real-time flight tests are applied on the Qball-X4 quadrotor. Different LoE actuator-fault scenarios are presented in this paper to validate the proposed algorithms. },
issn = {2373-6720},
language = {English},
publisher = {IEEE},
isbn = {978-1-7281-0334-1},
pages = {405-412}
}

Actuator fault is a prominent system’s issue that attracts the researchers’ attentions. One type of actuator fault is a constant Loss-of-Effectiveness (LoE). In this paper, two systematic algorithms are presented. The first algorithm is designed to detect and diagnosis the actuator fault. This Fault-Detection-and-Diagnosis (FDD) approach relies on an estimator design that is called intelligent Output-Estimator (iOE). The proposed iOE is intended to improve the estimation of the actuator fault based on the Model-Free technique. The second algorithm works online after detecting, isolating and estimating the actuator fault. This Active-Fault-Tolerant-Control (AFTC) algorithm will use the estimated actuator value in a reconfigured control design to compensate for the fault. Real-time flight tests are applied on the Qball-X4 quadrotor. Different LoE actuator-fault scenarios are presented in this paper to validate the proposed algorithms.

@article{ouyang_2019,
title = {Adaptive control based on neural networks for an uncertain 2-DOF helicopter system with input deadzone and output constraints},
author = {Ouyang, Y.; Dong, L.; Xue, L.; Sun, C.},
journal = {IEEE/CAA Journal of Automatica Sinica},
year = {2019},
month = {05},
volume = {6},
number = {3},
institution = {Southeast University, Nanjing, China},
abstract = {In this paper, a study of control for an uncertain 2-degree of freedom ( DOF ) helicopter system is given. The 2-DOF helicopter is subject to input deadzone and output constraints. In order to cope with system uncertainties and input deadzone, the neural network technique is introduced because of its capability in approximation. In order to update the weights of the neural network, an adaptive control method is utilized to improve the system adaptability. Furthermore, the integral barrier Lyapunov function ( IBLF ) is adopt in control design to guarantee the condition of output constraints and boundedness of the corresponding tracking errors. The Lyapunov direct method is applied in the control design to analyze system stability and convergence. Finally, numerical simulations are conducted to prove the feasibility and effectiveness of the proposed control based on the model of Quanser ʼ s 2-DOF helicopter. },
issn = {2329-9266 },
keywords = {2-degree of freedom (DOF) helicopter, adaptive control, input deadzone, integral barrier Lyapunov function, neural networks, output constraints},
language = {English},
publisher = {IEEE},
pages = {807-815}
}

In this paper, a study of control for an uncertain 2-degree of freedom ( DOF ) helicopter system is given. The 2-DOF helicopter is subject to input deadzone and output constraints. In order to cope with system uncertainties and input deadzone, the neural network technique is introduced because of its capability in approximation. In order to update the weights of the neural network, an adaptive control method is utilized to improve the system adaptability. Furthermore, the integral barrier Lyapunov function ( IBLF ) is adopt in control design to guarantee the condition of output constraints and boundedness of the corresponding tracking errors. The Lyapunov direct method is applied in the control design to analyze system stability and convergence. Finally, numerical simulations are conducted to prove the feasibility and effectiveness of the proposed control based on the model of Quanser ʼ s 2-DOF helicopter.

@article{perez-alcocer_2019,
title = {Adaptive Control for Quadrotor Trajectory Tracking With Accurate Parametrization},
author = {Pérez-Alcocer, R.; Moreno-Valenzuela, J.},
journal = {IEEE Access},
year = {2019},
volume = {7},
institution = {Instituto Politécnico Nacional–CITEDI, Mexico},
abstract = {In this paper, a novel adaptive controller for quadrotor position and orientation trajectory tracking is introduced. By taking into account the coupling between the position and the orientation dynamics, an adaptive scheme based on an accurate parameterization of the model-based feedforward compensation is presented. The adaptation update laws for the adaptation parameters are designed on Lyapunov’s theory so that the stability of the state space origin of the error dynamics is guaranteed. Barbalat’s lemma ensures convergence of the tracking errors and bounding of the adaptation parameters. The extensive real-time experimental results show the practical viability of the proposed scheme. More specifically, the performance of the proposed controller is compared with an adaptive controller taken from the literature and the non-adaptive version of the proposed controller. Better results are obtained with the novel adaptive approach. },
issn = {2169-3536 },
keywords = {Adaptive control, Lyapunov-theory, quadrotor, accurate parameterization},
language = {English},
publisher = {IEEE}
}

In this paper, a novel adaptive controller for quadrotor position and orientation trajectory tracking is introduced. By taking into account the coupling between the position and the orientation dynamics, an adaptive scheme based on an accurate parameterization of the model-based feedforward compensation is presented. The adaptation update laws for the adaptation parameters are designed on Lyapunov’s theory so that the stability of the state space origin of the error dynamics is guaranteed. Barbalat’s lemma ensures convergence of the tracking errors and bounding of the adaptation parameters. The extensive real-time experimental results show the practical viability of the proposed scheme. More specifically, the performance of the proposed controller is compared with an adaptive controller taken from the literature and the non-adaptive version of the proposed controller. Better results are obtained with the novel adaptive approach.

@article{boby_2019,
title = {Adaptive control of nonlinear system based on QFT application to 3-DOF flight control system},
author = {Boby, R.I.; Abdullah, K.; Jusoh, A.Z.; Parveen, N.; Mahmud, M.},
journal = {Telekomnika},
year = {2019},
month = {10},
volume = {17},
number = {5},
institution = {International Islamic University Malaysia},
abstract = {Research on unmanned aerial vehicle (UAV) became popular because of remote flight access and cost-effective solution. 3-degree of freedom (3-DOF) unmanned helicopters is one of the popular research UAV, because of its high load carrying capacity with a smaller number of motor and requirement of forethought motor control dynamics. Various control algorithms are investigated and designed for the motion control of the 3DOF helicopter. Three-degree-of-freedom helicopter model configuration presents the same advantages of 3-DOF helicopters along with increased payload capacity, increase stability in hover, manoeuvrability and reduced mechanical complexity. Numerous research institutes have chosen the three-degree-of-freedom as an ideal platform to develop intelligent controllers. In this research paper, we discussed about a hybrid controller that combined with Adaptive and Quantitative Feedback theory (QFT) controller for the 3-DOF helicopter model. Though research on Adaptive and QFT controller are not a new subject, the first successful single Adaptive aircraft flight control systems have been designed for the U.S. Air Force in Wright Laboratories unmanned research vehicle, Lambda [1]. Previously researcher focused on structured uncertainties associated with controller for the flight conditions theoretically. The development of simulation based design on flight control system response, opened a new dimension for researcher to design physical flight controller for plant parameter uncertainties. At the beginning, our research was to investigates the possibility of developing the QFT combined with Adaptive controller to control a single pitch angle that meets flying quality conditions of automatic flight control. Finally, we successfully designed the hybrid controller that is QFT based adaptive controller for all the three angles. },
issn = {1693-6930},
keywords = {3-degree of freedom (3-DOF), adaptive controller, hybride controller, quantitative feedback theory (QFT), UAV},
language = {English}
}

Research on unmanned aerial vehicle (UAV) became popular because of remote flight access and cost-effective solution. 3-degree of freedom (3-DOF) unmanned helicopters is one of the popular research UAV, because of its high load carrying capacity with a smaller number of motor and requirement of forethought motor control dynamics. Various control algorithms are investigated and designed for the motion control of the 3DOF helicopter. Three-degree-of-freedom helicopter model configuration presents the same advantages of 3-DOF helicopters along with increased payload capacity, increase stability in hover, manoeuvrability and reduced mechanical complexity. Numerous research institutes have chosen the three-degree-of-freedom as an ideal platform to develop intelligent controllers. In this research paper, we discussed about a hybrid controller that combined with Adaptive and Quantitative Feedback theory (QFT) controller for the 3-DOF helicopter model. Though research on Adaptive and QFT controller are not a new subject, the first successful single Adaptive aircraft flight control systems have been designed for the U.S. Air Force in Wright Laboratories unmanned research vehicle, Lambda [1]. Previously researcher focused on structured uncertainties associated with controller for the flight conditions theoretically. The development of simulation based design on flight control system response, opened a new dimension for researcher to design physical flight controller for plant parameter uncertainties. At the beginning, our research was to investigates the possibility of developing the QFT combined with Adaptive controller to control a single pitch angle that meets flying quality conditions of automatic flight control. Finally, we successfully designed the hybrid controller that is QFT based adaptive controller for all the three angles.

@article{chairez_2019,
title = {Adaptive control schemes applied to a control moment gyroscope of 2 degrees of freedom},
author = {Montoya–Cháirez, J.; Santibáñez, V.; Moreno–Valenzuela, J.},
journal = {Mechatronics},
year = {2019},
month = {02},
volume = {57},
institution = {Instituto Politécnico Nacional–CITEDI, Mexico; Instituto Tecnológico de La Laguna, Mexico},
abstract = {Control of underactuated mechanical systems has been an important trend in mechatronics and nonlinear systems. Typical examples are the Furuta pendulum and inertia wheel pendulum. On the other hand, gyroscopes are important in aerial and space systems. In this paper, a two-degree of freedom underactuated control moment gyroscope (CMG) is studied. This system is highly coupled of one joint to the another and is difficult to control, which make it an important benchmark system. The problem addressed in this paper is to achieve robust motion control of the underactuated CMG. Firstly, a trajectory tracking controller is developed by using the feedback linearization technique. Secondly, two new adaptive algorithms are introduced, which correspond to an adaptive neural network algorithm and an adaptive model regressor scheme. A real–time experimental comparison is carried out among a linear PD control law, a cascade PID-PID controller and the introduced schemes. The real-time experimental study validates the introduced theory, where the performance of the controllers is evaluated with and without a periodic disturbance at the input. },
keywords = {Control moment gyroscope, Underactuated systems, Trajectory tracking control, Adaptive control, Real–time experiments},
language = {English},
publisher = {Elsevier Ltd.},
pages = {73-85}
}

Control of underactuated mechanical systems has been an important trend in mechatronics and nonlinear systems. Typical examples are the Furuta pendulum and inertia wheel pendulum. On the other hand, gyroscopes are important in aerial and space systems. In this paper, a two-degree of freedom underactuated control moment gyroscope (CMG) is studied. This system is highly coupled of one joint to the another and is difficult to control, which make it an important benchmark system. The problem addressed in this paper is to achieve robust motion control of the underactuated CMG. Firstly, a trajectory tracking controller is developed by using the feedback linearization technique. Secondly, two new adaptive algorithms are introduced, which correspond to an adaptive neural network algorithm and an adaptive model regressor scheme. A real–time experimental comparison is carried out among a linear PD control law, a cascade PID-PID controller and the introduced schemes. The real-time experimental study validates the introduced theory, where the performance of the controllers is evaluated with and without a periodic disturbance at the input.

@article{niu_2019,
title = {Adaptive phase compensator for vibration suppression of structures with parameter perturbation},
author = {Niu, W.; Li, B.},
journal = {Aerospace Science and Technology},
year = {2019},
institution = {Northwestern Polytechnical University, China},
abstract = {The constant all-pass filter has been widely used for phase compensation of control systems. However, it is not applicable to compensating phase deviation caused by additional filters to enhance the signal quality in vibration control systems with parameter perturbation. To overcome this challenge, an adaptive phase compensator (APC) is developed by transforming the constant parameters of an all-pass filter into frequency-dependent parameters. In addition, the sources of phase deviations in the control system are analyzed to design the APC, including the additional filters, non-collocated actuator/sensor configuration, and hardware hysteresis. The phase deviations are determined through simulation and experiment. Polynomial fitting is implemented to obtain the APC parameters. To verify the feasibility of the proposed APC, numerical and experimental efforts are undertaken for buffeting suppression of the vertical tail, which is a typical structure with parameter perturbation. Both results demonstrate that the stability and robustness of control system adopting APC are strengthened compared to that of the control system using a constant phase compensator. Moreover, a control system that adopts APC can also effectively reduce the vibration response for structures with parameter perturbation under harmonic and random excitations. This performance improvement indicates that the proposed APC provides more effective compensation performance for the phase deviation of control systems with time-varying perturbations. },
keywords = {Adaptive phase compensator, Phase compensation for additional filter, Estimation of phase deviation, Structure with parameter perturbation, Vibration control of vertical tail},
language = {English},
publisher = {Elsevier Masson SAS}
}

The constant all-pass filter has been widely used for phase compensation of control systems. However, it is not applicable to compensating phase deviation caused by additional filters to enhance the signal quality in vibration control systems with parameter perturbation. To overcome this challenge, an adaptive phase compensator (APC) is developed by transforming the constant parameters of an all-pass filter into frequency-dependent parameters. In addition, the sources of phase deviations in the control system are analyzed to design the APC, including the additional filters, non-collocated actuator/sensor configuration, and hardware hysteresis. The phase deviations are determined through simulation and experiment. Polynomial fitting is implemented to obtain the APC parameters. To verify the feasibility of the proposed APC, numerical and experimental efforts are undertaken for buffeting suppression of the vertical tail, which is a typical structure with parameter perturbation. Both results demonstrate that the stability and robustness of control system adopting APC are strengthened compared to that of the control system using a constant phase compensator. Moreover, a control system that adopts APC can also effectively reduce the vibration response for structures with parameter perturbation under harmonic and random excitations. This performance improvement indicates that the proposed APC provides more effective compensation performance for the phase deviation of control systems with time-varying perturbations.

@article{zaare_2019,
title = {Adaptive sliding mode control of n flexible-joint robot manipulators in the presence of structured and unstructured uncertainties},
author = {Zaare, S.; Soltanpour, M.R.; Moattari, M.},
journal = {Multibody System Dynamics},
year = {2019},
institution = {Islamic Azad University, Iran; Shahid Sattari Aeronautical University of Science and Technology, Iran},
abstract = {This study investigates a voltage-based adaptive sliding mode control (VB-ASMC) to tracking the position of an n rigid-link flexible-joint (RLFJ) robot manipulator under the presence of uncertainties and external disturbances. First, the dynamic equations of the n-RLFJ robot manipulator have been divided into n subsystems, and for each of them a voltage-based sliding mode control (VB-SMC) is designed simultaneously. The mathematical proof shows that the closed-loop system under VB-SMC has global asymptotic stability. Second, due to the use of the sign function in the VB-SMC structure, the occurrence of chattering is inevitable. Therefore, to overcome this problem, an adaptive estimator is designed to estimate the boundary of uncertainties. Since the adaptive estimator part in the VB-ASMC has only one law, the proposed control has a very low computational volume. The Lyapunov stability theorem shows that the controlled closed-loop system under the VB-ASMC has global asymptotic stability. Finally, extensive simulations on the single and 2-RLFJ robot manipulator and practical implementation on the single-RLFJ robot manipulator are presented to demonstrate the effectiveness and improved performance of the proposed control scheme. },
issn = {1384-5640},
keywords = {Robot manipulator, Joint flexibility, Uncertainty, Chattering, Adaptive estimator, Adaptive sliding mode control },
language = {English},
publisher = {Springer Netherlands}
}

This study investigates a voltage-based adaptive sliding mode control (VB-ASMC) to tracking the position of an n rigid-link flexible-joint (RLFJ) robot manipulator under the presence of uncertainties and external disturbances. First, the dynamic equations of the n-RLFJ robot manipulator have been divided into n subsystems, and for each of them a voltage-based sliding mode control (VB-SMC) is designed simultaneously. The mathematical proof shows that the closed-loop system under VB-SMC has global asymptotic stability. Second, due to the use of the sign function in the VB-SMC structure, the occurrence of chattering is inevitable. Therefore, to overcome this problem, an adaptive estimator is designed to estimate the boundary of uncertainties. Since the adaptive estimator part in the VB-ASMC has only one law, the proposed control has a very low computational volume. The Lyapunov stability theorem shows that the controlled closed-loop system under the VB-ASMC has global asymptotic stability. Finally, extensive simulations on the single and 2-RLFJ robot manipulator and practical implementation on the single-RLFJ robot manipulator are presented to demonstrate the effectiveness and improved performance of the proposed control scheme.

@article{niu_2018,
title = {Adaptive vibration suppression of time-varying structures with enhanced FxLMS algorithm},
author = {Niu, W.; Zou, C.; Li, B.; Wanga, W.},
journal = {Mechanical Systems and Signal Processing},
year = {2019},
month = {03},
volume = {118},
institution = {Northwestern Polytechnical University, China; Ohio State University, USA},
abstract = {Filtered-x least mean square (FxLMS) control algorithm has been widely used for adaptive noise and vibration control. Yet, classical FxLMS controller is not applicable for time-varying system since the secondary path identified offline cannot reflect the system characteristics in real-time. In order to overcome this challenge, here online modelling of secondary path is realized by existing signals, i.e. no noise injection. In addition, FxLMS is further enhanced with variable step size that is adaptively adjusted via bang-bang controller. The adaptation of step size is aimed to achieve the balance between control efficiency and system stability. To verify the feasibility of proposed control algorithm, numerical and experimental efforts are undertaken for the buffeting suppression of vertical tail which is a typical time-varying system. Both results demonstrate that the proposed method is able to reduce the vibration response effectively for varied structures under harmonic and random excitations, while the classical FxLMS cannot. This performance improvement indicates that the online modeling of secondary path captures the system characteristics accurately and timely. Moreover, compared with the FxLMS controller with fixed step size, the control efficiency of the proposed method is also strengthened. These multiple enhancements of the performance of FxLMS controller reveal that online modeling and variable step size are favorable for adaptive vibration control of time-varying structures. },
keywords = {Filtered-x least mean square, Bang-bang control, Variable step size, Online identification, Time-varying structures},
language = {English},
publisher = {Elsevier Ltd.}
}

Filtered-x least mean square (FxLMS) control algorithm has been widely used for adaptive noise and vibration control. Yet, classical FxLMS controller is not applicable for time-varying system since the secondary path identified offline cannot reflect the system characteristics in real-time. In order to overcome this challenge, here online modelling of secondary path is realized by existing signals, i.e. no noise injection. In addition, FxLMS is further enhanced with variable step size that is adaptively adjusted via bang-bang controller. The adaptation of step size is aimed to achieve the balance between control efficiency and system stability. To verify the feasibility of proposed control algorithm, numerical and experimental efforts are undertaken for the buffeting suppression of vertical tail which is a typical time-varying system. Both results demonstrate that the proposed method is able to reduce the vibration response effectively for varied structures under harmonic and random excitations, while the classical FxLMS cannot. This performance improvement indicates that the online modeling of secondary path captures the system characteristics accurately and timely. Moreover, compared with the FxLMS controller with fixed step size, the control efficiency of the proposed method is also strengthened. These multiple enhancements of the performance of FxLMS controller reveal that online modeling and variable step size are favorable for adaptive vibration control of time-varying structures.

@article{cao_2019,
title = {ADRC-Based Current Control for Grid-Tied Inverters: Design, Analysis, and Verification},
author = {Cao, Y.; Zhao, Q.; Ye, Y.; Xiong, Y.},
journal = { IEEE Transactions on Industrial Electronics},
year = {2019},
institution = {Nanjing University of Aeronautics and Astronautics, China},
abstract = {The conversion and utilisation of renewable energy generations often require grid-tied inverters. When an LCL filter is applied to attenuate the high switching frequency harmonics, it is complex to design a controller with proper parameters due to the characteristics of the LCL filter and system uncertainties. In this paper, with LCCL filter, the order of the inverter system is degraded from third-order to first-order, and an ADRC-based current control strategy for LCCL-type grid-tied inverters is proposed. The proposed strategy is able to treat the unknown dynamics and the external disturbance of the inverter system as overall disturbance through a single structure, and the closedloop system is regulated by an improved control law with reference differential feedforward. Moreover, with parameter uncertainties considered, the robustness of the proposed strategy is studied through an internal model control structure. A 2-kW experimental prototype has been tested to verify the effectiveness of the proposed scheme. },
issn = {0278-0046},
keywords = {Active disturbance rejection control (ADRC), LCCL filter, inverter, current control, internal model control (IMC) structure},
language = {English},
publisher = {IEEE}
}

The conversion and utilisation of renewable energy generations often require grid-tied inverters. When an LCL filter is applied to attenuate the high switching frequency harmonics, it is complex to design a controller with proper parameters due to the characteristics of the LCL filter and system uncertainties. In this paper, with LCCL filter, the order of the inverter system is degraded from third-order to first-order, and an ADRC-based current control strategy for LCCL-type grid-tied inverters is proposed. The proposed strategy is able to treat the unknown dynamics and the external disturbance of the inverter system as overall disturbance through a single structure, and the closedloop system is regulated by an improved control law with reference differential feedforward. Moreover, with parameter uncertainties considered, the robustness of the proposed strategy is studied through an internal model control structure. A 2-kW experimental prototype has been tested to verify the effectiveness of the proposed scheme.

@article{li4_2019,
title = {An Adaptive Distributed Consensus Control Algorithm Based on Continuous Terminal Sliding Model for Multiple Quad Rotors’ Formation Tracking},
author = {Li, Y.; Jiu, M.; Sun, Q.; Dong Q.},
journal = {IEEE Access},
year = {2019},
institution = {Harbin Engineering University, China},
abstract = {Distributed consensus formation tracking problem of slow convergence and low accuracy for multiple quad rotors are solved in this paper. Continuous nonsingular terminal sliding model algorithm (CNTSMA) under Inner-Outer loop control structure is considered to deal with the control problem of nonlinear, strong coupling and underactuated characteristic of a quad rotor. For better convergence and higher tracking accuracy, an adaptive algorithm (denoted by ACNTSMA) further focused on coupling of x and y directions, with sliding model observer (SMO) to compensate system disturbances and uncertainties is proposed. Furthermore, to solve the consensus formation tracking problem, a consensus control protocol under a full-connected topology based on ACNTSMA is proposed, including formation pattern consensus controller and formation distance consensus controller. Stability theory and simulation results prove the proposed control algorithm is able to accurately achieve the first-order (position and velocity) consensus for multiple quad rotors’ formation tracking. Besides, stability analysis of the proposed consensus control protocol illustrates that all sliding modes with finite-time stability applicable to second-order integral models are supposed to be established. And the proposed controller also has better convergence and faster response speed with no chattering for trajectory tracking. },
issn = {2169-3536 },
keywords = {Distributed formation tracking, consensus, continuous terminal sliding model algorithm, adaptive algorithm, multiple quad rotors, convergence},
language = {English},
publisher = {IEEE}
}

Distributed consensus formation tracking problem of slow convergence and low accuracy for multiple quad rotors are solved in this paper. Continuous nonsingular terminal sliding model algorithm (CNTSMA) under Inner-Outer loop control structure is considered to deal with the control problem of nonlinear, strong coupling and underactuated characteristic of a quad rotor. For better convergence and higher tracking accuracy, an adaptive algorithm (denoted by ACNTSMA) further focused on coupling of x and y directions, with sliding model observer (SMO) to compensate system disturbances and uncertainties is proposed. Furthermore, to solve the consensus formation tracking problem, a consensus control protocol under a full-connected topology based on ACNTSMA is proposed, including formation pattern consensus controller and formation distance consensus controller. Stability theory and simulation results prove the proposed control algorithm is able to accurately achieve the first-order (position and velocity) consensus for multiple quad rotors’ formation tracking. Besides, stability analysis of the proposed consensus control protocol illustrates that all sliding modes with finite-time stability applicable to second-order integral models are supposed to be established. And the proposed controller also has better convergence and faster response speed with no chattering for trajectory tracking.

@article{sun_2019,
title = {An artificial muscle driven by decomposition of gas solution},
author = {Sun, E.; Wang, T.},
journal = {Smart Materials and Structures},
year = {2019},
institution = { Zhejiang University, China},
abstract = {This note develops an artificial muscle driven by the decomposition of gas solution. It mainly consists of a silicone tube, a resistive wire, a braided mesh shell, and an amount gas solution which can be decomposed reversibly by temperature variation. It can be operated directly by supplying the resistive wire with controllable electrical power, which is different from traditional fluidic artificial muscles requiring external pressure generating devices. To verify the effectiveness of the artificial muscle, prototypes are fabricated and the process is presented in detail. Ammonia solution is employed in the prototypes due to its high solubility and low decomposition temperature. Experiments under both of isometric and isotonic contractions are implemented and the results show that the prototypes can achieve good mechanical performance. },
keywords = {Artificial muscle, fluid power, gas solution, decomposition, isometric and isotonic contractions},
language = {English},
publisher = {IOP Publishing}
}

This note develops an artificial muscle driven by the decomposition of gas solution. It mainly consists of a silicone tube, a resistive wire, a braided mesh shell, and an amount gas solution which can be decomposed reversibly by temperature variation. It can be operated directly by supplying the resistive wire with controllable electrical power, which is different from traditional fluidic artificial muscles requiring external pressure generating devices. To verify the effectiveness of the artificial muscle, prototypes are fabricated and the process is presented in detail. Ammonia solution is employed in the prototypes due to its high solubility and low decomposition temperature. Experiments under both of isometric and isotonic contractions are implemented and the results show that the prototypes can achieve good mechanical performance.

@article{falcon_2019,
title = {An Attractive Ellipsoid-based Robust Control for Quad-Rotor Tracking},
author = {Falcon, R.; Rios, H.; Mera, M.; Dzul, A.},
journal = {IEEE Transactions on Industrial Electronics },
year = {2019},
institution = {Tecnologico Nacional de Mexico, Mexico; Instituto Politecnico Nacional, Mexico},
abstract = {This paper deals with the tracking control problem for a Quad-Rotor in the presence of external disturbances and only by means of the measurable positions and angles. To this aims, a constructive output-based robust control approach is designed based on: 1) slidingmode observation theory, for state estimation; and 2) the attractive ellipsoid method, for control design. The closedloop robust stability is proven through Lyapunov methods. The synthesis of the robust output-based control approach is expressed in terms of Linear Matrix Inequalities. Experimental results on the QBall2 platform by Quanser illustrate the feasibility of the proposed approach. },
issn = {0278-0046 },
keywords = {Robust Output-based Control, Quad-Rotor, Sliding-Mode Observers},
language = {English},
publisher = {IEEE}
}

This paper deals with the tracking control problem for a Quad-Rotor in the presence of external disturbances and only by means of the measurable positions and angles. To this aims, a constructive output-based robust control approach is designed based on: 1) slidingmode observation theory, for state estimation; and 2) the attractive ellipsoid method, for control design. The closedloop robust stability is proven through Lyapunov methods. The synthesis of the robust output-based control approach is expressed in terms of Linear Matrix Inequalities. Experimental results on the QBall2 platform by Quanser illustrate the feasibility of the proposed approach.