@article{mohanty_2020,
title = {3 DOF Autonomous Control Analysis of an Quadcopter Using Artificial Neural Network},
author = {Mohanty S.; Misra A. },
booktitle = {Modern Approaches in Machine Learning and Cognitive Science: A Walkthrough.},
year = {2020},
institution = {Defense Institute of Advanced TechnologyPuneIndia},
abstract = {The Quadcopter is an Unmanned Aerial Vehicle (UAV) which has turned out to be exceptionally mainstream among specialists in the recent past due to the advantages it offers over conventional helicopters. Quadcopter is extremely unique and interesting, however it is inherently unsteady from streamlined features perspective and aerodynamics point of view. In recent past scientists have proposed many control schemes for the stability of quadcopter, but Artificial Neural Network (ANN) systems provide us with the fusion of human intelligence, logic and reasoning. The research focuses on the use of ANN for the control plant systems whose plant dynamics are expensive to model, inaccurate or change with time and environment. In this paper, we explore the Linear Quadratic Regulator (LQR) and Sliding Mode Control (SMC) control is designed for an quadcopter with 3 Degree Of freedom (DOF) Hover model by Quancer. The main benefits of this approach are the model’s ability of adapt quickly to unmodeled aerodynamics, disturbances, component failure due to battle damage, etc. It eliminates the costs and time associated with the wind tunnel testing and generation of control derivatives for the UAV’s. },
keywords = {Quadcopter, Unmanned aerial vehicle (UAV), Artificial neural network (ANN), Linear quadratic regulator (LQR), Sliding mode control (SMC)},
language = {English},
series = {Studies in Computational Intelligence},
publisher = {Springer, Cham},
isbn = {978-3-030-38444-9},
pages = {39-57}
}

The Quadcopter is an Unmanned Aerial Vehicle (UAV) which has turned out to be exceptionally mainstream among specialists in the recent past due to the advantages it offers over conventional helicopters. Quadcopter is extremely unique and interesting, however it is inherently unsteady from streamlined features perspective and aerodynamics point of view. In recent past scientists have proposed many control schemes for the stability of quadcopter, but Artificial Neural Network (ANN) systems provide us with the fusion of human intelligence, logic and reasoning. The research focuses on the use of ANN for the control plant systems whose plant dynamics are expensive to model, inaccurate or change with time and environment. In this paper, we explore the Linear Quadratic Regulator (LQR) and Sliding Mode Control (SMC) control is designed for an quadcopter with 3 Degree Of freedom (DOF) Hover model by Quancer. The main benefits of this approach are the model’s ability of adapt quickly to unmodeled aerodynamics, disturbances, component failure due to battle damage, etc. It eliminates the costs and time associated with the wind tunnel testing and generation of control derivatives for the UAV’s.

This paper describes a novel stereo vision sensor based on deep neural networks, that can be used to produce a feedback signal for visual servoing in unmanned aerial vehicles such as drones. Two deep convolutional neural networks attached to the stereo camera in the drone are trained to detect wind turbines in images and stereo triangulation is used to calculate the distance from a wind turbine to the drone. Our experimental results show that the sensor produces data accurate enough to be used for servoing, even in the presence of noise generated when the drone is not being completely stable. Our results also show that appropriate filtering of the signals is needed and that to produce correct results, it is very important to keep the wind turbine within the field of vision of both cameras, so that both deep neural networks could detect it.

@conference{lutter_2020,
title = {A Differentiable Newton Euler Algorithm for Multi-body Model Learning},
author = {Lutter, M.; Silberbauer, J.; Watson, J.; Peters, J.},
booktitle = {International Conference on Machine Learning (ICML)},
year = {2020},
institution = {Technical University of Darmstadt, Germany},
abstract = {In this work, we examine a spectrum of hybrid models for the domain of multibody robot dynamics. We motivate a computation graph architecture that embodies the Newton Euler equations, emphasising the utility of the Lie Algebra form in translating the dynamical geometry into an efficient computational structure for learning (Handa et al., 2016). We describe the used actuator models (Section 3) and the virtual parameters (Appendix). In the experiments, we evaluate 26 Newton-Euler based system identification approaches and benchmark these models on the simulated and physical Furuta Pendulum and Cartpole. The comparison shows that the kinematic parameters, required by previous Newton-Euler methods (Atkeson et al., 1986; Sutanto et al., 2020; Ledezma & Haddadin, 2017), can be accurately inferred from data. Furthermore, we highlight that models with guaranteed bounded energy of the uncontrolled system generate non-divergent trajectories, while more general models have no such guarantee. Therefore, their performance strongly depends on the data distribution. The main contributions of this work are the introduction of a white-box model that jointly learns dynamic and kinematics parameters and can be combined with black-box components. We then provide an extensive empirical evaluation on challenging systems and different datasets that elucidates the comparative performance of our grey-box architecture with comparable white and black-box models. },
language = {English}
}

In this work, we examine a spectrum of hybrid models for the domain of multibody robot dynamics. We motivate a computation graph architecture that embodies the Newton Euler equations, emphasising the utility of the Lie Algebra form in translating the dynamical geometry into an efficient computational structure for learning (Handa et al., 2016). We describe the used actuator models (Section 3) and the virtual parameters (Appendix). In the experiments, we evaluate 26 Newton-Euler based system identification approaches and benchmark these models on the simulated and physical Furuta Pendulum and Cartpole. The comparison shows that the kinematic parameters, required by previous Newton-Euler methods (Atkeson et al., 1986; Sutanto et al., 2020; Ledezma & Haddadin, 2017), can be accurately inferred from data. Furthermore, we highlight that models with guaranteed bounded energy of the uncontrolled system generate non-divergent trajectories, while more general models have no such guarantee. Therefore, their performance strongly depends on the data distribution. The main contributions of this work are the introduction of a white-box model that jointly learns dynamic and kinematics parameters and can be combined with black-box components. We then provide an extensive empirical evaluation on challenging systems and different datasets that elucidates the comparative performance of our grey-box architecture with comparable white and black-box models.

@article{wang3_2020,
title = {A dual adaptive fault-tolerant control for a quadrotor helicopter against actuator faults and model uncertainties without overestimation},
author = {Wang, B.; Yu, X.; Mu, L.; Zhang, Y.},
journal = {Aerospace Science and Technology},
year = {2020},
month = {4},
volume = {99},
institution = {Northwestern Polytechnical University, China; Beihang University, China; Xi'an University of Technology, China, Concordia University, Canada},
abstract = {This paper presents a dual adaptive fault-tolerant control strategy for a quadrotor helicopter based on adaptive sliding mode control and adaptive boundary layer. Within the proposed adaptive control strategy, both model uncertainties and actuator faults can be compensated without the knowledge of the uncertainty bounds and fault information. By virtue of the proposed adaptive control scheme, the minimum discontinuous control gain is adopted, which significantly reduces the control chattering effect. As compared to the existing adaptive sliding mode control schemes in the literature, larger actuator faults can be tolerated by employing the proposed control scheme while suppressing control chattering. Moreover, boundary layer is used to smoothen control discontinuity and further eliminate control chattering. Nevertheless, the choice of boundary layer thickness is a trade-off between system stability and tracking accuracy. By explicitly considering this fact, an adaptive boundary layer is developed and synthesized with the proposed adaptive control framework to ensure stability and tracking accuracy of the considered system. When the control parameter tends to be overestimated, the thickness of boundary layer can be appropriately adjusted to avoid control parameter overestimation. Simulation and experimental tests of a quadrotor helicopter are both conducted to validate the effectiveness of the proposed control scheme. Its advantages are demonstrated in comparison with a conventional adaptive sliding mode control scheme. },
keywords = {Actuator fault, Adaptive sliding mode control, Fault-tolerant control, Model uncertainty, Quadrotor helicopter},
language = {English},
publisher = {Elsevier Masson}
}

This paper presents a dual adaptive fault-tolerant control strategy for a quadrotor helicopter based on adaptive sliding mode control and adaptive boundary layer. Within the proposed adaptive control strategy, both model uncertainties and actuator faults can be compensated without the knowledge of the uncertainty bounds and fault information. By virtue of the proposed adaptive control scheme, the minimum discontinuous control gain is adopted, which significantly reduces the control chattering effect. As compared to the existing adaptive sliding mode control schemes in the literature, larger actuator faults can be tolerated by employing the proposed control scheme while suppressing control chattering. Moreover, boundary layer is used to smoothen control discontinuity and further eliminate control chattering. Nevertheless, the choice of boundary layer thickness is a trade-off between system stability and tracking accuracy. By explicitly considering this fact, an adaptive boundary layer is developed and synthesized with the proposed adaptive control framework to ensure stability and tracking accuracy of the considered system. When the control parameter tends to be overestimated, the thickness of boundary layer can be appropriately adjusted to avoid control parameter overestimation. Simulation and experimental tests of a quadrotor helicopter are both conducted to validate the effectiveness of the proposed control scheme. Its advantages are demonstrated in comparison with a conventional adaptive sliding mode control scheme.

@article{reyes-baez_2020,
title = {A family of virtual contraction based controllers for tracking of flexible-joints port-Hamiltonian robots: theory and experiments},
author = {Reyes-Baez, R.; van der Schaft, A.; Jayawardhana, B.; Pan, l.},
journal = {arXiv},
year = {2020},
institution = {University of Groningen, The Netherlands},
abstract = {In this work we present a constructive method to design a family of virtual contraction based controllers that solve the standard trajectory tracking problem of flexible-joint robots (FJRs) in the port-Hamiltonian (pH) framework. The proposed design method, called virtual contraction based control (v-CBC), combines the concepts of virtual control systems and contraction analysis. It is shown that under potential energy matching conditions, the closed-loop virtual system is contractive and exponential convergence to a predefined trajectory is guaranteed. Moreover, the closed-loop virtual system exhibits properties such as structure preservation, differential passivity and the existence of (incrementally) passive maps. },
keywords = {Flexible-joints robots, tracking control, port-Hamiltonian systems, contraction, virtual control systems},
language = {English}
}

In this work we present a constructive method to design a family of virtual contraction based controllers that solve the standard trajectory tracking problem of flexible-joint robots (FJRs) in the port-Hamiltonian (pH) framework. The proposed design method, called virtual contraction based control (v-CBC), combines the concepts of virtual control systems and contraction analysis. It is shown that under potential energy matching conditions, the closed-loop virtual system is contractive and exponential convergence to a predefined trajectory is guaranteed. Moreover, the closed-loop virtual system exhibits properties such as structure preservation, differential passivity and the existence of (incrementally) passive maps.

@conference{srinivasa_2020,
title = {A Hardware Proof of Concept of Networked Adaptive Systems},
author = {Srinivasa, S.B.; Makam, R.; George, K.},
booktitle = {2020 6th International Conference on Control, Automation and Robotics (ICCAR)},
year = {2020},
institution = {PES University, India},
abstract = {A hardware proof of concept of adaptation over the internet to achieve stability and output tracking despite the presence of both packet dropouts and delays induced by the network is provided in this paper. Assuming that the parameters of the models of the physical systems are unknown, a modified model reference adaptive control law for such networked adaptive systems ensures that the outputs of these systems asymptotically track specified desired trajectories. Further, we demonstrate that the multiple models, switching, and tuning methodology improves the transient performance. The example systems considered here are the Linear Servo Base Unit and the Qube Servo. },
keywords = {adaptive control, discrete-time systems, communication network, linear system},
language = {English},
publisher = {IEEE},
isbn = {978-1-7281-6140-2}
}

A hardware proof of concept of adaptation over the internet to achieve stability and output tracking despite the presence of both packet dropouts and delays induced by the network is provided in this paper. Assuming that the parameters of the models of the physical systems are unknown, a modified model reference adaptive control law for such networked adaptive systems ensures that the outputs of these systems asymptotically track specified desired trajectories. Further, we demonstrate that the multiple models, switching, and tuning methodology improves the transient performance. The example systems considered here are the Linear Servo Base Unit and the Qube Servo.

@conference{hernandez_2020,
title = {A Machine Vision Framework for Autonomous Inspection of Drilled Holes in CFRP Panels},
author = {Hernandez, A.; Maghami, A.; Khoshdarregi, M. },
booktitle = {2020 6th International Conference on Control, Automation and Robotics (ICCAR)},
year = {2020},
institution = {Technological Institute of Veracruz, Mexico; University of Manitoba, Canada},
abstract = {This paper presents a fully autonomous framework for the inspection of drilled holes in planar carbon fiber composite panels used in the aerospace and automotive industries. The proposed framework can automatically recognize a part and extract the geometrical information from an existing library of DXF files. It then determines the location and orientation of the part with respect to the motion platform without a need for explicit programming of the part coordinate system. Visual servoing and optimal motion planning techniques are used to autonomously move the end-effector's camera to each hole to capture high resolution images. Image processing techniques are used to determine the geometrical errors and delamination factors for each hole. All of the proposed computer vision modules have been implemented in Python and OpenCV, which are open source and thus readily available to the industry. Experimental results prove that the proposed framework can efficiently and autonomously inspect drilled holes in composite panels with minimal programming required of the end-user. },
keywords = {vision-based inspection, visual servoing, drilled hole, aerospace panel, composite material, machine vision},
language = {English},
publisher = {IEEE},
isbn = {978-1-7281-6140-2}
}

This paper presents a fully autonomous framework for the inspection of drilled holes in planar carbon fiber composite panels used in the aerospace and automotive industries. The proposed framework can automatically recognize a part and extract the geometrical information from an existing library of DXF files. It then determines the location and orientation of the part with respect to the motion platform without a need for explicit programming of the part coordinate system. Visual servoing and optimal motion planning techniques are used to autonomously move the end-effector's camera to each hole to capture high resolution images. Image processing techniques are used to determine the geometrical errors and delamination factors for each hole. All of the proposed computer vision modules have been implemented in Python and OpenCV, which are open source and thus readily available to the industry. Experimental results prove that the proposed framework can efficiently and autonomously inspect drilled holes in composite panels with minimal programming required of the end-user.

@article{zhu_2020,
title = {A Model-Based Approach for Measurement Noise Estimation and Compensation in Feedback Control Systems},
author = {Zhu, Y.; Zhu, B.; Yang Zhu; Bo Zhu; Qin, K.; Liu, H.H.T.},
journal = { IEEE Transactions on Instrumentation and Measurement},
year = {2020},
institution = {University of Electronic Science and Technology of China, China; University of Toronto Institute for Aerospace Studies, Canada},
abstract = {This paper considers the problem of measurement noise rejection in a linear output-feedback control system. Specifically, we take into account not only the rejection of high-frequency stochastic noises, but also the compensation for low-frequency measurement errors like bias and drift which cannot be well-handled by classic frequency domain filters or Kalman filters. A novel noise estimator (NE)-based robust control solution is proposed. The NE is designed in the frequency domain by exploiting the system model and control structure information, and is embedded into the controller instead of being an independent functional module in the closed-loop system. The adverse effects of model uncertainties on the performance of NE-based solution are investigated, and an improved solution is proposed by incorporating a simple low-pass filter as the pre-filter of NE. This solution is applied to the angle tracking problem of a 2-DOF experimental helicopter platform equipped with a low-cost and low-accuracy MEMS IMU for angular position/rate measurements. Both numerical simulation and experimental comparisons with other existing approaches demonstrate: (i) constant bias and time-varying drift in the IMU measurements are systematically addressed by the solution; (ii) it is accessible to improve the steady-state tracking accuracy by tuning the parameter of NE to extend its bandwidth; and (iii) when model uncertainties limit the feasible bandwidth of NE, the improved solution is able to largely maintain its noise rejection performance. },
issn = {0018-9456},
keywords = {Low-cost sensors, MEMS IMU, measurement noise estimation, bias and drift compensation, output feedback systems, robust control},
language = {English},
publisher = {IEEE}
}

This paper considers the problem of measurement noise rejection in a linear output-feedback control system. Specifically, we take into account not only the rejection of high-frequency stochastic noises, but also the compensation for low-frequency measurement errors like bias and drift which cannot be well-handled by classic frequency domain filters or Kalman filters. A novel noise estimator (NE)-based robust control solution is proposed. The NE is designed in the frequency domain by exploiting the system model and control structure information, and is embedded into the controller instead of being an independent functional module in the closed-loop system. The adverse effects of model uncertainties on the performance of NE-based solution are investigated, and an improved solution is proposed by incorporating a simple low-pass filter as the pre-filter of NE. This solution is applied to the angle tracking problem of a 2-DOF experimental helicopter platform equipped with a low-cost and low-accuracy MEMS IMU for angular position/rate measurements. Both numerical simulation and experimental comparisons with other existing approaches demonstrate: (i) constant bias and time-varying drift in the IMU measurements are systematically addressed by the solution; (ii) it is accessible to improve the steady-state tracking accuracy by tuning the parameter of NE to extend its bandwidth; and (iii) when model uncertainties limit the feasible bandwidth of NE, the improved solution is able to largely maintain its noise rejection performance.

@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{jiang_2020,
title = {A New Impedance Control Method Using Backstepping Approach for Flexible Joint Robot Manipulators},
author = {Jiang, Z.-H.; Irie, T.},
journal = {International Journal of Mechanical Engineering and Robotics Research},
year = {2020},
month = {06},
volume = {9},
number = {6},
institution = {Hiroshima Institute of Technology, Japan; THK Corporation, Japan},
abstract = {In this paper, we propose a new impedance control method for flexible joint robot manipulators. An ideal nonlinear impedance dynamic model is formulated in the workspace. Three control strategies that meet the requirement of desired impedance dynamics and stability of the whole system are derived by using backstepping control approach. The control system has a cascade structure with the designed three control strategies serially connecting to each other. Stability of the closed-loop system is analyzed using Lyapunov stability theory. Impedance control experiments are carried out on a 2-link flexible joint robot manipulator with a force sensor equipped at the end-effector. The results demonstrate the effectiveness of the proposed impedance control method.  },
keywords = {impedance control, workspace, flexible joint robot, backstepping approach, stability analysis},
language = {English},
publisher = {Int. J. Mech. Eng. Rob. Res}
}

In this paper, we propose a new impedance control method for flexible joint robot manipulators. An ideal nonlinear impedance dynamic model is formulated in the workspace. Three control strategies that meet the requirement of desired impedance dynamics and stability of the whole system are derived by using backstepping control approach. The control system has a cascade structure with the designed three control strategies serially connecting to each other. Stability of the closed-loop system is analyzed using Lyapunov stability theory. Impedance control experiments are carried out on a 2-link flexible joint robot manipulator with a force sensor equipped at the end-effector. The results demonstrate the effectiveness of the proposed impedance control method. 

@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{chen_2020,
title = {A novel cable-suspended quadrotor transportation system: From theory to experiment},
author = {Chen, T.; Shan, J.},
journal = {Aerospace Science and Technology},
year = {2020},
month = {09},
volume = {104},
institution = {York University, Canada},
abstract = {This paper studies the development of a novel quadrotor aerial transportation system, which carries payload with four cables. The possible stable configurations are discussed to show the advantages of the four-cable system. The bounds of the disturbance forces and torques acting on the quadrotor from the payload are estimated based on the dynamics analysis. Then a hierarchical adaptive controller is designed on the Lie group SO(3) for the transportation of the payload by the quadrotor. Finally, experimental results are presented to show the effectiveness of the proposed transportation system and the controller. },
keywords = {Quadrotor, Cable-suspended payload, Hierarchical adaptive robust control, UAV transportation system},
language = {English},
publisher = {Elsevier Masson}
}

This paper studies the development of a novel quadrotor aerial transportation system, which carries payload with four cables. The possible stable configurations are discussed to show the advantages of the four-cable system. The bounds of the disturbance forces and torques acting on the quadrotor from the payload are estimated based on the dynamics analysis. Then a hierarchical adaptive controller is designed on the Lie group SO(3) for the transportation of the payload by the quadrotor. Finally, experimental results are presented to show the effectiveness of the proposed transportation system and the controller.

@article{singh_2020,
title = {A novel fault classification‐based fault‐tolerant control for two degree of freedom helicopter systems},
author = {Singh, R.; Bhushan, B.},
journal = {International Journal of Adaptive Control and Signal Processing},
year = {2020},
institution = {Delhi Technological University, India},
abstract = {Fault detection and diagnosis (FDD) plays an essential role in identifying and isolating various faults in a system. In general, fault detection is attained by monitoring the extent of matching between the actual operating condition and an analytical model prediction. This process aids in achieving enhanced performance, and for operating the system within the acceptable bounds. In this article, a neural network‐based classification method and fuzzy‐based control strategy are adapted to perform FDD on a two degree of freedom (2DoF) helicopter system. The operating voltage, pitch, and yaw outputs of the 2DoF helicopter system were considered for developing the algorithm. The signal processing properties of the discrete wavelet transform and pattern recognition properties of a multilayer perceptron neural network are adapted to design the classification algorithm. The developed algorithm improves training and testing efficiency. In order to reinstate the normal operation of the system, the classifier output is integrated with a hybrid fuzzy‐proportional integral derivative controller. This control technique enhances the 2DoF helicopter response as the time taken by the pitch and yaw angle to settle trajectory is reduced. The results depicted validate the efficiency of the projected approach. },
keywords = {2DoF helicopter, discrete wavelet transform, fault detection and diagnosis, fuzzy-proportional integral derivative, multilayer perceptron neural network},
language = {English},
publisher = {John Wiley & Sons, Inc.}
}

Fault detection and diagnosis (FDD) plays an essential role in identifying and isolating various faults in a system. In general, fault detection is attained by monitoring the extent of matching between the actual operating condition and an analytical model prediction. This process aids in achieving enhanced performance, and for operating the system within the acceptable bounds. In this article, a neural network‐based classification method and fuzzy‐based control strategy are adapted to perform FDD on a two degree of freedom (2DoF) helicopter system. The operating voltage, pitch, and yaw outputs of the 2DoF helicopter system were considered for developing the algorithm. The signal processing properties of the discrete wavelet transform and pattern recognition properties of a multilayer perceptron neural network are adapted to design the classification algorithm. The developed algorithm improves training and testing efficiency. In order to reinstate the normal operation of the system, the classifier output is integrated with a hybrid fuzzy‐proportional integral derivative controller. This control technique enhances the 2DoF helicopter response as the time taken by the pitch and yaw angle to settle trajectory is reduced. The results depicted validate the efficiency of the projected approach.

@article{mera_2020,
title = {A sliding-mode based controller for trajectory tracking of perturbed Unicycle Mobile Robots},
author = {Mera, M.; Ríos, H.; Martínez, E.A.},
journal = {Control Engineering Practice},
year = {2020},
month = {09},
volume = {102},
institution = {Instituto Politécnico Nacional, Mexico; Tecnológico Nacional de México/I.T. La Laguna, Mexico},
abstract = {In this work a tracking robust algorithm for the perturbed kinematic model of an Unicycle Mobile Robot (UMR) is proposed. The control design is based on the well-known first order sliding mode control approach, with a modification that helps to reduce the chattering effect. This strategy takes into account perturbations and consider any admissible (with respect to the nonholonomic constraints) smooth reference trajectory, ensuring the convergence of the tracking error dynamics to the origin asymptotically. The resulting control input is a discontinuous switched function. Its implementability is validated through experiments using a QBot2 and compared with standard well-established control design methods for this problem. },
keywords = {Sliding-modes control, Mobile Robots, Tracking},
language = {English},
publisher = {Elsevier Ltd.}
}

In this work a tracking robust algorithm for the perturbed kinematic model of an Unicycle Mobile Robot (UMR) is proposed. The control design is based on the well-known first order sliding mode control approach, with a modification that helps to reduce the chattering effect. This strategy takes into account perturbations and consider any admissible (with respect to the nonholonomic constraints) smooth reference trajectory, ensuring the convergence of the tracking error dynamics to the origin asymptotically. The resulting control input is a discontinuous switched function. Its implementability is validated through experiments using a QBot2 and compared with standard well-established control design methods for this problem.

@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.

@article{wang2_2020,
title = {Active fault-tolerant control for a quadrotor helicopter against actuator faults and model uncertainties},
author = {Wang, B.; Shen, Y.; Zhang, Y.},
journal = {Aerospace Science and Technology},
year = {2020},
month = {04},
volume = {99},
institution = {Northwestern Polytechnical University, China; Concordia University, Canada},
abstract = {This paper proposes an active fault-tolerant control strategy for a quadrotor helicopter against actuator faults and model uncertainties while explicitly considering fault estimation errors based on adaptive sliding mode control and recurrent neural networks. Firstly, a novel adaptive sliding mode control is proposed. In virtue of the proposed adaptive schemes, the system tracking performance can be guaranteed in the presence of model uncertainties without stimulating control chattering. Then, due to the fact that model-based fault estimation schemes may fail to correctly estimate fault magnitudes in the presence of model uncertainties, a fault estimation scheme is proposed by designing a parallel bank of recurrent neural networks. With the trained networks, the severity of actuator faults can be precisely estimated. Finally, by synthesizing the proposed fault estimation scheme with the developed adaptive sliding mode control, an active fault-tolerant control mechanism is established. Moreover, the issue of actuator fault estimation error is explicitly considered and compensated by the proposed adaptive sliding mode control. The effectiveness of the proposed active fault-tolerant control strategy is validated through real experiments based on a quadrotor helicopter subject to actuator faults and model uncertainties. Its advantages are demonstrated in comparison with a model-based fault estimator and a conventional adaptive sliding mode control. },
keywords = {Actuator fault estimation, Active fault-tolerant control, Adaptive sliding mode control, Model uncertainties, Recurrent neural network},
language = {English},
publisher = {Elsevier Masson}
}

This paper proposes an active fault-tolerant control strategy for a quadrotor helicopter against actuator faults and model uncertainties while explicitly considering fault estimation errors based on adaptive sliding mode control and recurrent neural networks. Firstly, a novel adaptive sliding mode control is proposed. In virtue of the proposed adaptive schemes, the system tracking performance can be guaranteed in the presence of model uncertainties without stimulating control chattering. Then, due to the fact that model-based fault estimation schemes may fail to correctly estimate fault magnitudes in the presence of model uncertainties, a fault estimation scheme is proposed by designing a parallel bank of recurrent neural networks. With the trained networks, the severity of actuator faults can be precisely estimated. Finally, by synthesizing the proposed fault estimation scheme with the developed adaptive sliding mode control, an active fault-tolerant control mechanism is established. Moreover, the issue of actuator fault estimation error is explicitly considered and compensated by the proposed adaptive sliding mode control. The effectiveness of the proposed active fault-tolerant control strategy is validated through real experiments based on a quadrotor helicopter subject to actuator faults and model uncertainties. Its advantages are demonstrated in comparison with a model-based fault estimator and a conventional adaptive sliding mode control.

@article{goncalves_2020,
title = {Active vibration control in a two degrees of freedom structure using piezoelectric transducers associated with negative capacitance shunt circuits},
author = {Goncalves, A.; Almeida, A.; de Moura, E.; da Rocha Souto, C.; Ries, A.},
journal = {International Journal of Dynamics and Control},
year = {2020},
institution = {Federal University of Paraiba, Brazil},
abstract = {The need for control or suppression of vibrations in mechanical structures has arisen because of their damaging effects on people, civil structures and machine parts. The present work aims to perform the vibration control of a portico type structure with two degrees of freedom, using piezoelectric transducers associated with negative capacitance shunt circuits with series electrical resistance. For this purpose, an electronic circuit with passive and active components associated with piezoelectric transducers QP10W was developed to produce a negative capacitance shunt circuit, implemented through Negative Impedance Converters (NIC) and using operational amplifiers. The response amplitudes of the system in the time domain and the frequency in free and forced vibration were analyzed in tests performed with and without shunt circuit operation in the system. Considering free vibration, a reduction of 9.01 dB was obtained for the first natural frequency and of 6.95 dB for the second one. For forced vibration, reductions of 1.5 dB were obtained for the first natural frequency and 2.19 dB for the second natural frequency, respectively. The vibration reductions obtained with the proposed system demonstrate the efficiency of the system. },
keywords = {Vibration control, Piezoelectric transducers, Negative capacitance shunt circuit, Negative impedance converters},
language = {English},
publisher = {Springer-Verlag GmBH}
}

The need for control or suppression of vibrations in mechanical structures has arisen because of their damaging effects on people, civil structures and machine parts. The present work aims to perform the vibration control of a portico type structure with two degrees of freedom, using piezoelectric transducers associated with negative capacitance shunt circuits with series electrical resistance. For this purpose, an electronic circuit with passive and active components associated with piezoelectric transducers QP10W was developed to produce a negative capacitance shunt circuit, implemented through Negative Impedance Converters (NIC) and using operational amplifiers. The response amplitudes of the system in the time domain and the frequency in free and forced vibration were analyzed in tests performed with and without shunt circuit operation in the system. Considering free vibration, a reduction of 9.01 dB was obtained for the first natural frequency and of 6.95 dB for the second one. For forced vibration, reductions of 1.5 dB were obtained for the first natural frequency and 2.19 dB for the second natural frequency, respectively. The vibration reductions obtained with the proposed system demonstrate the efficiency of the system.

@article{yang_2020,
title = {Adaptive Sliding Mode Fault-Tolerant Control for Uncertain Systems with Time Delay},
author = {Yang, P.; Liu, Z.; Wang, Y.; Li, D.},
journal = {International Journal of Automation Technology},
year = {2020},
volume = {14},
number = {2},
institution = {Nanjing University of Aeronautics and Astronautics, China; Chinese Flight Test Establishment, China},
abstract = {In this work, an adaptive sliding mode fault-tolerant controller is proposed for a class of uncertain systems with time delay. The integral term is added to the traditional sliding surface to improve the robustness of the control system, and then a type of special sliding surface is designed to cancel the reaching mode based on global sliding mode method. Without the need for fault detection and isolation, an adaptive law is proposed to estimate the value of actuator faults, and an adaptive sliding mode fault-tolerant controller is designed to guarantee the asymptotic stability of sliding dynamics. Finally, the presented control scheme is applied to the position control of a Qball-X4 quad-rotor UAV model to verify the effectiveness. },
issn = {1881-7629},
keywords = {fault-tolerant control, adaptive estimation, time delay, sliding mode control, uncertain systems},
language = {English},
publisher = {Fuji Technology Press}
}

In this work, an adaptive sliding mode fault-tolerant controller is proposed for a class of uncertain systems with time delay. The integral term is added to the traditional sliding surface to improve the robustness of the control system, and then a type of special sliding surface is designed to cancel the reaching mode based on global sliding mode method. Without the need for fault detection and isolation, an adaptive law is proposed to estimate the value of actuator faults, and an adaptive sliding mode fault-tolerant controller is designed to guarantee the asymptotic stability of sliding dynamics. Finally, the presented control scheme is applied to the position control of a Qball-X4 quad-rotor UAV model to verify the effectiveness.

@article{ren_2_2020,
title = {Advising reinforcement learning toward scaling agents in continuous control environments with sparse rewards},
author = {Ren, H.; Ben-Tzvi, P.},
journal = {Engineering Applications of Artificial Intelligence},
year = {2020},
month = {04},
volume = {90},
institution = {Virginia Tech, USA},
abstract = {This paper adapts the success of the teacher–student framework for reinforcement learning to a continuous control environment with sparse rewards. Furthermore, the proposed advising framework is designed for the scaling agents problem, wherein the student policy is trained to control multiple agents while the teacher policy is well trained for a single agent. Existing research on teacher–student frameworks have been focused on discrete control domain. Moreover, they rely on similar target and source environments and as such they do not allow for scaling the agents. On the other hand, in this work the agents face a scaling agents problem where the value functions of the source and target task converge at different rates. Existing concepts from the teacher–student framework are adapted to meet new challenges including early advising, importance of advising, and mistake correction, but a modified heuristic was used to decide on when to teach. The performance of the proposed algorithm was evaluated using the case study of pushing, and picking and placing objects with a dual arm manipulation system. The teacher policy was trained using a simulated scenario consisting of a single arm. The student policy was trained to handle the dual arm manipulation system in simulation under the advice of the teacher agent. The trained student policy was then validated using two Quanser Mico arms for experimental demonstration. The effects of varying parameters on the student performance in the advising framework was also analyzed and discussed. The results showed that the proposed advising framework expedited the training process and achieved the desired scaling within a limited advising budget. },
keywords = {Reinforcement learning, Advising framework, Continuous control, Sparse reward, Multi-agent},
language = {English},
publisher = {Elsevier Ltd.}
}

This paper adapts the success of the teacher–student framework for reinforcement learning to a continuous control environment with sparse rewards. Furthermore, the proposed advising framework is designed for the scaling agents problem, wherein the student policy is trained to control multiple agents while the teacher policy is well trained for a single agent. Existing research on teacher–student frameworks have been focused on discrete control domain. Moreover, they rely on similar target and source environments and as such they do not allow for scaling the agents. On the other hand, in this work the agents face a scaling agents problem where the value functions of the source and target task converge at different rates. Existing concepts from the teacher–student framework are adapted to meet new challenges including early advising, importance of advising, and mistake correction, but a modified heuristic was used to decide on when to teach. The performance of the proposed algorithm was evaluated using the case study of pushing, and picking and placing objects with a dual arm manipulation system. The teacher policy was trained using a simulated scenario consisting of a single arm. The student policy was trained to handle the dual arm manipulation system in simulation under the advice of the teacher agent. The trained student policy was then validated using two Quanser Mico arms for experimental demonstration. The effects of varying parameters on the student performance in the advising framework was also analyzed and discussed. The results showed that the proposed advising framework expedited the training process and achieved the desired scaling within a limited advising budget.

@article{sun_2020,
title = {An experimental study of bellows-type fluidic soft bending actuators under external water pressure},
author = {Sun, E.; Wang, T.; Zhu, S.},
journal = { Smart Materials and Structures},
year = {2020},
institution = {Zhejiang University, China},
abstract = {Soft actuators which are composed of low-modulus material have broad application prospects in marine exploration tasks such as biological sampling due to their inherent compliance and adaptability. However, the influence of underwater pressure on the soft actuators remains to be studied. In this work, an experimental study of bellows-type fluidic soft bending actuators fabricated with 3D printing technology is implemented. Deep sea test environment is simulated by adjustable external water pressure. The response of the soft actuator to the input pressure under different external pressure (from 1 atm to 15 MPa) is presented and discussed. The results show that the external water pressure can cause the actuator to bend more in both static and dynamic conditions. Moreover, the increase of the bending angle is positively related with the environment pressure. In general, the feasibility of the soft actuators and the fluid power system for underwater applications are verified. This work can provide experimental reference for the design and control of soft manipulators in marine operation. },
keywords = {Fluidic soft actuators, underwater application, environment pressure},
language = {English},
publisher = {IOP Publishing}
}

Soft actuators which are composed of low-modulus material have broad application prospects in marine exploration tasks such as biological sampling due to their inherent compliance and adaptability. However, the influence of underwater pressure on the soft actuators remains to be studied. In this work, an experimental study of bellows-type fluidic soft bending actuators fabricated with 3D printing technology is implemented. Deep sea test environment is simulated by adjustable external water pressure. The response of the soft actuator to the input pressure under different external pressure (from 1 atm to 15 MPa) is presented and discussed. The results show that the external water pressure can cause the actuator to bend more in both static and dynamic conditions. Moreover, the increase of the bending angle is positively related with the environment pressure. In general, the feasibility of the soft actuators and the fluid power system for underwater applications are verified. This work can provide experimental reference for the design and control of soft manipulators in marine operation.

@inbook{de-abreu_2020,
title = {An Implementable and Stabilizing Model Predictive Control Strategy for Inverted Pendulum-Like Behaved Systems},
author = {de Abreu, O.S.L.; Martins, M.A.F.; Schnitman, L.},
booktitle = {Inverted Pendulum},
year = {2020},
institution = {Universidade Federal da Bahia, Brazil},
abstract = {In control theory, the inverted pendulum is a class of dynamic systems widely used as a benchmarking for evaluating several control strategies. Such a system is characterized by an underactuated behavior. It is also nonlinear and presents open-loop unstable and integrating modes. These dynamic features make the control more difficult, mainly when the controller synthesis seeks to include constraints and the guarantee of stability of the closed-loop system. This chapter presents a stabilizing model predictive control (MPC) strategy for inverted pendulum-like behaved systems. It has an offset-free control law based on an only optimization problem (one-layer control formulation), and the Lyapunov stability of the closed-loop system is achieved by adopting an infinite prediction horizon. The controller feasibility is also assured by imposing a suitable set of slacked terminal constraints associated with the unstable and integrating states of the system. The effectiveness of the implementable and stabilizing MPC controller is experimentally demonstrated in a commercial-didactic rotary inverted pendulum prototype, considering both cases of stabilization of the pendulum in the upright position and the output tracking of the rotary arm angle. },
keywords = {rotary inverted pendulum, model predictive control, nonlinear system, Lyapunov stability, feasible-optimization problem},
language = {English},
publisher = {IntechOpen}
}

In control theory, the inverted pendulum is a class of dynamic systems widely used as a benchmarking for evaluating several control strategies. Such a system is characterized by an underactuated behavior. It is also nonlinear and presents open-loop unstable and integrating modes. These dynamic features make the control more difficult, mainly when the controller synthesis seeks to include constraints and the guarantee of stability of the closed-loop system. This chapter presents a stabilizing model predictive control (MPC) strategy for inverted pendulum-like behaved systems. It has an offset-free control law based on an only optimization problem (one-layer control formulation), and the Lyapunov stability of the closed-loop system is achieved by adopting an infinite prediction horizon. The controller feasibility is also assured by imposing a suitable set of slacked terminal constraints associated with the unstable and integrating states of the system. The effectiveness of the implementable and stabilizing MPC controller is experimentally demonstrated in a commercial-didactic rotary inverted pendulum prototype, considering both cases of stabilization of the pendulum in the upright position and the output tracking of the rotary arm angle.

@article{silva_2020,
title = {An implementable stabilizing model predictive controller applied to a rotary flexible link: An experimental case study},
author = {Silva, B.P.M.; Santana, B.A.; Santos, T.L.M.; Martins, M.A.F.},
journal = {Control Engineering Practice},
year = {2020},
month = {06},
volume = {99},
institution = {Universidade Federal da Bahia, Brazil},
abstract = {This work addresses the application of implementable stabilizing model predictive control (MPC) strategies to a rotary flexible link (RFL). Despite their practice usefulness and design simplicity, the implementation of these stable MPC controllers with guaranteed feasibility in a real experiment, and dedicated to dynamic features of RFL systems, have not yet been documented by the literature. In contrast to conventional finite-horizon MPC strategies, infinite-horizon MPC (IHMPC) techniques ensure nominal closed-loop stability irrespective of the cost function parameters. Also, if these IHMPC strategies have feasible-optimization problem based formulations, such as implementable ones investigated here, their applicability naturally becomes attractive to the industry. Simulation and experimental results illustrate the usefulness of the implementable stabilizing MPC controllers when compared to non-feasible in practice ones, such as a classical infinite-horizon MPC and a conventional finite-horizon generalized predictive controller, thus ensuring their benefits in terms of performance and computational burden in the context of constrained RFL mechatronic systems. },
keywords = {Model predictive control, Rotary flexible link, Closed-loop stability, Feasible-optimization problem},
language = {English},
publisher = {Elsevier Ltd.}
}

This work addresses the application of implementable stabilizing model predictive control (MPC) strategies to a rotary flexible link (RFL). Despite their practice usefulness and design simplicity, the implementation of these stable MPC controllers with guaranteed feasibility in a real experiment, and dedicated to dynamic features of RFL systems, have not yet been documented by the literature. In contrast to conventional finite-horizon MPC strategies, infinite-horizon MPC (IHMPC) techniques ensure nominal closed-loop stability irrespective of the cost function parameters. Also, if these IHMPC strategies have feasible-optimization problem based formulations, such as implementable ones investigated here, their applicability naturally becomes attractive to the industry. Simulation and experimental results illustrate the usefulness of the implementable stabilizing MPC controllers when compared to non-feasible in practice ones, such as a classical infinite-horizon MPC and a conventional finite-horizon generalized predictive controller, thus ensuring their benefits in terms of performance and computational burden in the context of constrained RFL mechatronic systems.

@article{sharif_2020,
title = {Assist-as-needed Policy for Movement Therapy Using Telerobotics-mediated Therapist Supervision},
author = {Sharifi, M.; Behzadipour, S.; Salarieh, H.; Tavakoli, M.},
journal = {Control Engineering Practice},
year = {2020},
month = {08},
volume = {101},
institution = {University of Alberta, Canada; Sharif University of Technology, Iran},
abstract = {In this paper, a new impedance-based teleoperation strategy is proposed for assist-as-needed tele-rehabilitation via a multi-DOF telerobotic system having patient–master and therapist–slave interactions. Unlike a regular teleoperation system and as the main contribution of this work to minimize the therapist’s movements, the therapist’s hand only follows the patient’s deviation from the target trajectory. Also it provides a better perception of the patient’s problems in motor control to the therapist The admissible deviation of the patient’s limb from a reference target trajectory is governed by an impedance model responding to both patient’s and therapist’s interaction forces. As the other benefit of this framework, two sources of assistance to the patient are delivered through the master robot: (1) the adjustable impedance model, and (2) the force applied by the therapist to the slave robot. The assistive impedance model is beneficial to reduce magnitudes of the required force from the therapist and decrease his/her intervention. This results in delaying and declining the therapist’s muscle fatigue in time-consuming movement therapies. Bilateral nonlinear control laws with two types of adaptation laws are designed for the nonlinear teleoperation system. The Lyapunov stability proof of the teleoperation system and the stability of the impedance model enhance the patient’s and therapist’s safety even in the presence of modeling uncertainties of the multi-DOF telerobotic system. The performance of the proposed bilateral impedance-based strategy is experimentally investigated using different impedance parameters adjusted based on the patient’s characteristics (e.g., involuntary tremor) and disabilities (e.g., insufficient actuation force). The experiments are performed by a healthy person (as the therapist), a mechanical test bed and a volunteer (simulating the patients’ characteristics). A new force–position mapping from Cartesian to Normal–Tangential (N–T) coordinates is utilized between the master and slave workspaces and compared with typical Cartesian to Cartesian projection. },
keywords = {Assist-as-needed tele-rehabilitation, Patient–therapist collaboration, Bilateral telerobotic system, Impedance control, Nonlinear adaptive control, Lyapunov stability},
language = {English},
publisher = {Elsevier Ltd.}
}

In this paper, a new impedance-based teleoperation strategy is proposed for assist-as-needed tele-rehabilitation via a multi-DOF telerobotic system having patient–master and therapist–slave interactions. Unlike a regular teleoperation system and as the main contribution of this work to minimize the therapist’s movements, the therapist’s hand only follows the patient’s deviation from the target trajectory. Also it provides a better perception of the patient’s problems in motor control to the therapist The admissible deviation of the patient’s limb from a reference target trajectory is governed by an impedance model responding to both patient’s and therapist’s interaction forces. As the other benefit of this framework, two sources of assistance to the patient are delivered through the master robot: (1) the adjustable impedance model, and (2) the force applied by the therapist to the slave robot. The assistive impedance model is beneficial to reduce magnitudes of the required force from the therapist and decrease his/her intervention. This results in delaying and declining the therapist’s muscle fatigue in time-consuming movement therapies. Bilateral nonlinear control laws with two types of adaptation laws are designed for the nonlinear teleoperation system. The Lyapunov stability proof of the teleoperation system and the stability of the impedance model enhance the patient’s and therapist’s safety even in the presence of modeling uncertainties of the multi-DOF telerobotic system. The performance of the proposed bilateral impedance-based strategy is experimentally investigated using different impedance parameters adjusted based on the patient’s characteristics (e.g., involuntary tremor) and disabilities (e.g., insufficient actuation force). The experiments are performed by a healthy person (as the therapist), a mechanical test bed and a volunteer (simulating the patients’ characteristics). A new force–position mapping from Cartesian to Normal–Tangential (N–T) coordinates is utilized between the master and slave workspaces and compared with typical Cartesian to Cartesian projection.

@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{schleer_2020,
title = {Augmentation of haptic feedback for teleoperated robotic surgery},
author = {Schleer, P.; Kaiser, P.; Drobinsky, S.; Radermacher, K.},
journal = {International Journal of Computer Assisted Radiology and Surgery },
year = {2020},
institution = {Helmholtz Institute for Biomedical Engineering, Germany},
abstract = {Purpose: A frequently mentioned lack of teleoperated surgical robots is the lack of haptic feedback. Haptics are not only able to mirror force information from the situs, but also to provide spatial guidance according to a surgical plan. However, superposition of the two haptic information can lead to overlapping and masking of the feedback and guidance forces. This study investigates different approaches toward a combination of both information and investigates effects on system usability. Methods: Preliminary studies are conducted to define parameters for two main experiments. The two main experiments constitute simulated surgical interventions where haptic guidance as well as haptic feedback provide information for the surgeon. The first main experiment considers drilling for pedicle screw placements, while the second main experiment refers to three-dimensional milling tasks such as during partial knee replacements or craniectomies. For both experiments, different guidance modes in combination with haptic feedback are evaluated regarding effectiveness (e.g., distance to target depth), efficiency and user satisfaction (e.g., detectability of discrepancies in case of technical guidance error). Results: Regarding pedicle screw placements a combination of a peripheral visual signal and a vibration constitutes a good compromise regarding distance to target depth and detectability of discrepancies. For milling tasks, trajectory guidance is able to improve efficiency and user satisfaction (e.g., perceived workload), while boundary constraints improve effectiveness. If, assistance cannot be offered in all degrees of freedom (e.g., craniectomies), a visual substitution of the haptic force feedback shows the best results, though participants prefer using haptic force feedback. Conclusion: Our results suggest that in case haptic feedback and haptic assistance are combined appropriately, benefits of both haptic modalities can be exploited. Thereby, capabilities of the human–machine system are improved compared to usage of exclusively one of the haptic information. },
language = {English},
publisher = {Springer Nature Switzerland}
}

Purpose: A frequently mentioned lack of teleoperated surgical robots is the lack of haptic feedback. Haptics are not only able to mirror force information from the situs, but also to provide spatial guidance according to a surgical plan. However, superposition of the two haptic information can lead to overlapping and masking of the feedback and guidance forces. This study investigates different approaches toward a combination of both information and investigates effects on system usability.

Methods: Preliminary studies are conducted to define parameters for two main experiments. The two main experiments constitute simulated surgical interventions where haptic guidance as well as haptic feedback provide information for the surgeon. The first main experiment considers drilling for pedicle screw placements, while the second main experiment refers to three-dimensional milling tasks such as during partial knee replacements or craniectomies. For both experiments, different guidance modes in combination with haptic feedback are evaluated regarding effectiveness (e.g., distance to target depth), efficiency and user satisfaction (e.g., detectability of discrepancies in case of technical guidance error).

Results: Regarding pedicle screw placements a combination of a peripheral visual signal and a vibration constitutes a good compromise regarding distance to target depth and detectability of discrepancies. For milling tasks, trajectory guidance is able to improve efficiency and user satisfaction (e.g., perceived workload), while boundary constraints improve effectiveness. If, assistance cannot be offered in all degrees of freedom (e.g., craniectomies), a visual substitution of the haptic force feedback shows the best results, though participants prefer using haptic force feedback.

Conclusion: Our results suggest that in case haptic feedback and haptic assistance are combined appropriately, benefits of both haptic modalities can be exploited. Thereby, capabilities of the human–machine system are improved compared to usage of exclusively one of the haptic information.

@article{morales-valdez_2020,
title = {Automated damage location for building structures using the hysteretic model and frequency domain neural networks},
author = {Morales-Valdez, J.; Lopez-Pacheco, M.; Yu, W.},
journal = {Structural Control Health Monitoring},
year = {2020},
institution = {CONACyT, Mexico; CINVESTAV-IPN, Mexico},
abstract = {his paper presents a novel and accurate model‐reference health monitoring system for the location of damage to building structures using the dissipated energy approach, frequency domain convolutional neural networks (CNNs), and principal component analysis (PCA). Due to the fact that the earthquake introduces several stress cycles in different directions in the structure, load–strain curves can be used as an indicator of damage. The CNN in the frequency domain (CNNFI) is used to estimate the hysteretic displacement of the reference of the Bouc–Wen model. Automated damage locations are resolved with the CNN classification models (CNNFC). The comparison study for damage location is presented by using classical neural networks. The results of the damage location of a two‐story building prototype confirmed that the proposed method is promising for real applications. },
keywords = {Structural health monitoring, damage location, convolutional neural network, principal component analysis},
language = {English},
publisher = {John Wiley & Sons, Inc.}
}

his paper presents a novel and accurate model‐reference health monitoring system for the location of damage to building structures using the dissipated energy approach, frequency domain convolutional neural networks (CNNs), and principal component analysis (PCA). Due to the fact that the earthquake introduces several stress cycles in different directions in the structure, load–strain curves can be used as an indicator of damage. The CNN in the frequency domain (CNNFI) is used to estimate the hysteretic displacement of the reference of the Bouc–Wen model. Automated damage locations are resolved with the CNN classification models (CNNFC). The comparison study for damage location is presented by using classical neural networks. The results of the damage location of a two‐story building prototype confirmed that the proposed method is promising for real applications.

@article{li_2020,
title = {AutoTrack: Towards High-Performance Visual Tracking for UAV with Automatic Spatio-Temporal Regularization},
author = {Li, Y.; Fu, C.; Ding, F.; Huang, Z.; Lu, G.},
journal = {arXiv},
year = {2020},
institution = {Tongji University, China; National University of Singapore, Singapore; Tsinghua University, China},
abstract = {Most existing trackers based on discriminative correlation filters (DCF) try to introduce predefined regularization term to improve the learning of target objects, e.g., by suppressing background learning or by restricting change rate of correlation filters. However, predefined parameters introduce much effort in tuning them and they still fail to adapt to new situations that the designer did not think of. In this work, a novel approach is proposed to online automatically and adaptively learn spatio-temporal regularization term. Spatially local response map variation is introduced as spatial regularization to make DCF focus on the learning of trust-worthy parts of the object, and global response map variation determines the updating rate of the filter. Extensive experiments on four UAV benchmarks have proven the superiority of our method compared to the state-of-the-art CPU- and GPU-based trackers, with a speed of ~60 frames per second running on a single CPU. Our tracker is additionally proposed to be applied in UAV localization. Considerable tests in the indoor practical scenarios have proven the effectiveness and versatility of our localization method. The code is available at https://github.com/vision4robotics/AutoTrack },
language = {English}
}

Most existing trackers based on discriminative correlation filters (DCF) try to introduce predefined regularization term to improve the learning of target objects, e.g., by suppressing background learning or by restricting change rate of correlation filters. However, predefined parameters introduce much effort in tuning them and they still fail to adapt to new situations that the designer did not think of. In this work, a novel approach is proposed to online automatically and adaptively learn spatio-temporal regularization term. Spatially local response map variation is introduced as spatial regularization to make DCF focus on the learning of trust-worthy parts of the object, and global response map variation determines the updating rate of the filter. Extensive experiments on four UAV benchmarks have proven the superiority of our method compared to the state-of-the-art CPU- and GPU-based trackers, with a speed of ~60 frames per second running on a single CPU.
Our tracker is additionally proposed to be applied in UAV localization. Considerable tests in the indoor practical scenarios have proven the effectiveness and versatility of our localization method. The code is available at https://github.com/vision4robotics/AutoTrack

@article{koksal_2020,
title = {Backstepping-based adaptive control of a quadrotor UAV with guaranteed tracking performance},
author = {Koksal, N.; An, H.; Fidan, B.},
journal = {ISA Transactions},
year = {2020},
institution = {University of Waterloo, Canada; Harbin Institute of Technology, China},
abstract = {In this paper, a backstepping based indirect adaptive control design and an alternative direct adaptive control scheme, both with guaranteed transient and steady-state tracking performances, are proposed for trajectory tracking of a quadrotor unmanned aerial vehicle (UAV). Backstepping techniques, combined with a prescribed performance function based error transformation, are employed in both designs to achieve the bounded transient and steady-state tracking errors of the strict-feedback position system which comprises both lateral position and altitude dynamics. The effects of parametric inertia and drag uncertainties on attitude regulation are compensated using a least squares based parameter identification algorithm in the indirect adaptive control design, and using a constructive Lyapunov analysis approach in the direct adaptive control scheme. The stability of the closed-loop system for both designs is proven via Lyapunov analysis. Simulation and experimental test results are provided to verify the effectiveness of the proposed control designs. },
keywords = {Prescribed performance bound, Backstepping control, Adaptive control, LS parameter identification, Quadrotor UAV},
language = {English},
publisher = {Elsevier Ltd.}
}

In this paper, a backstepping based indirect adaptive control design and an alternative direct adaptive control scheme, both with guaranteed transient and steady-state tracking performances, are proposed for trajectory tracking of a quadrotor unmanned aerial vehicle (UAV). Backstepping techniques, combined with a prescribed performance function based error transformation, are employed in both designs to achieve the bounded transient and steady-state tracking errors of the strict-feedback position system which comprises both lateral position and altitude dynamics. The effects of parametric inertia and drag uncertainties on attitude regulation are compensated using a least squares based parameter identification algorithm in the indirect adaptive control design, and using a constructive Lyapunov analysis approach in the direct adaptive control scheme. The stability of the closed-loop system for both designs is proven via Lyapunov analysis. Simulation and experimental test results are provided to verify the effectiveness of the proposed control designs.

@article{muratore_2020,
title = {Bayesian Domain Randomization for Sim-to-Real Transfer},
author = {Muratore, F.; Eilers, C.; Gienger, M.; Peters, J.},
journal = {arXiv},
year = {2020},
institution = {Technical University Darmstadt, Germany; Honda Research Institute Europe, Germany},
abstract = {When learning policies for robot control, the real-world data required is typically prohibitively expensive to acquire, so learning in simulation is a popular strategy. Unfortunately, such polices are often not transferable to the real world due to a mismatch between the simulation and reality, called 'reality gap'. Domain randomization methods tackle this problem by randomizing the physics simulator (source domain) according to a distribution over domain parameters during training in order to obtain more robust policies that are able to overcome the reality gap. Most domain randomization approaches sample the domain parameters from a fixed distribution. This solution is suboptimal in the context of sim-to-real transferability, since it yields policies that have been trained without explicitly optimizing for the reward on the real system (target domain). Additionally, a fixed distribution assumes there is prior knowledge about the uncertainty over the domain parameters. Thus, we propose Bayesian Domain Randomization (BayRn), a black box sim-to-real algorithm that solves tasks efficiently by adapting the domain parameter distribution during learning by sampling the real-world target domain. BayRn utilizes Bayesian optimization to search the space of source domain distribution parameters which produce a policy that maximizes the real-word objective, allowing for adaptive distributions during policy optimization. We experimentally validate the proposed approach by comparing against two baseline methods on a nonlinear under-actuated swing-up task. Our results show that BayRn is capable to perform direct sim-to-real transfer, while significantly reducing the required prior knowledge. },
keywords = {machine learning},
language = {English}
}

When learning policies for robot control, the real-world data required is typically prohibitively expensive to acquire, so learning in simulation is a popular strategy. Unfortunately, such polices are often not transferable to the real world due to a mismatch between the simulation and reality, called 'reality gap'. Domain randomization methods tackle this problem by randomizing the physics simulator (source domain) according to a distribution over domain parameters during training in order to obtain more robust policies that are able to overcome the reality gap. Most domain randomization approaches sample the domain parameters from a fixed distribution. This solution is suboptimal in the context of sim-to-real transferability, since it yields policies that have been trained without explicitly optimizing for the reward on the real system (target domain). Additionally, a fixed distribution assumes there is prior knowledge about the uncertainty over the domain parameters. Thus, we propose Bayesian Domain Randomization (BayRn), a black box sim-to-real algorithm that solves tasks efficiently by adapting the domain parameter distribution during learning by sampling the real-world target domain. BayRn utilizes Bayesian optimization to search the space of source domain distribution parameters which produce a policy that maximizes the real-word objective, allowing for adaptive distributions during policy optimization. We experimentally validate the proposed approach by comparing against two baseline methods on a nonlinear under-actuated swing-up task. Our results show that BayRn is capable to perform direct sim-to-real transfer, while significantly reducing the required prior knowledge.

@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{wang_2020,
title = {Bounded UDE-based Controller for Input Constrained Systems with Uncertainties and Disturbances},
author = {Wang, Y.; Ren, B.; Zhong, Q.-C.},
journal = {IEEE Transactions on Industrial Electronics},
year = {2020},
institution = {Texas Tech University, USA; Illinois Institute of Technology, USA},
abstract = {The uncertainty and disturbance estimator (UDE)-based controller has emerged as an effective robust control method to handle systems with uncertainties and disturbances. However, similar to other controllers including an integral action, the UDE-based controller faces the integral windup issue when the system has input constraints. In this paper, a bounded UDE-based controller is proposed to deal with systems subject to uncertainties, disturbances and input constraint. An additional time-varying variable is introduced into the design of the error dynamics to naturally avoid integral windup. The boundedness design guarantees that both the final controller output and the additional time-varying variable dynamically move on an ellipse so that the controller output always satisfies the input constraint. The proposed bounded UDE-based controller inherits the robustness of the conventional UDE-based control method, and has a clear structure, with guidelines provided for parameter selections. Both theoretical analysis and experimental results are provided to validate the proposed design. },
issn = {0278-0046},
keywords = {Uncertainty and disturbance estimator (UDE), boundedness design, input constraint, integral windup},
language = {English},
publisher = {IEEE}
}

The uncertainty and disturbance estimator (UDE)-based controller has emerged as an effective robust control method to handle systems with uncertainties and disturbances. However, similar to other controllers including an integral action, the UDE-based controller faces the integral windup issue when the system has input constraints. In this paper, a bounded UDE-based controller is proposed to deal with systems subject to uncertainties, disturbances and input constraint. An additional time-varying variable is introduced into the design of the error dynamics to naturally avoid integral windup. The boundedness design guarantees that both the final controller output and the additional time-varying variable dynamically move on an ellipse so that the controller output always satisfies the input constraint. The proposed bounded UDE-based controller inherits the robustness of the conventional UDE-based control method, and has a clear structure, with guidelines provided for parameter selections. Both theoretical analysis and experimental results are provided to validate the proposed design.

@article{rsetam_2020,
title = {Cascaded Extended State Observer Based Sliding Mode Control for Underactuated Flexible Joint Robot},
author = {Rsetam, K.A.; Cao, Z.; Man, Z.},
journal = {IEEE Transactions on Industrial Electronics},
year = {2020},
institution = {Swinburne University of Technology, Australia},
abstract = {This paper presents a new cascaded extended state observer (CESO) based sliding mode control (SMC) for an underactuated flexible joint robot (FJR). The control of FJR has many challenges including coupling, underactuation, nonlinearity, uncertainties and external disturbances, and the noise amplification especially in the high order systems. The proposed control integrates CESO and SMC, in which the CESO estimates the states and disturbances, and the SMC provides the system robustness to the uncertainty and disturbance estimation errors. Firstly, a dynamic model of the FJR is derived and converted from an underactuated form to a canonical form via the Olfati transformation and flatness approach, which reduces the complexity of the controller design. Furthermore, by taking the advantage of available measurable states, the CESO is adopted to attenuate the noises and make SMC feasible for high order systems. Moreover, the CESO estimates the disturbances, which relaxes the upper bound of the disturbance in the SMC and reduces the chattering due to smaller switching gains. A stability analysis of the closed loop system is presented based on the Lyapunov method. The effectiveness of the proposed control is verified experimentally on a real time FJR system. },
issn = {1557-9948},
keywords = {Flexible Joint Robot (FJR), underactuation, cascade extended state observer (CESO), sliding mode Control (SMC)},
language = {English},
publisher = {IEEE}
}

This paper presents a new cascaded extended state observer (CESO) based sliding mode control (SMC) for an underactuated flexible joint robot (FJR). The control of FJR has many challenges including coupling, underactuation, nonlinearity, uncertainties and external disturbances, and the noise amplification especially in the high order systems. The proposed control integrates CESO and SMC, in which the CESO estimates the states and disturbances, and the SMC provides the system robustness to the uncertainty and disturbance estimation errors. Firstly, a dynamic model of the FJR is derived and converted from an underactuated form to a canonical form via the Olfati transformation and flatness approach, which reduces the complexity of the controller design. Furthermore, by taking the advantage of available measurable states, the CESO is adopted to attenuate the noises and make SMC feasible for high order systems. Moreover, the CESO estimates the disturbances, which relaxes the upper bound of the disturbance in the SMC and reduces the chattering due to smaller switching gains. A stability analysis of the closed loop system is presented based on the Lyapunov method. The effectiveness of the proposed control is verified experimentally on a real time FJR system.

@article{khorshid_2020,
title = {Command shaping with reduced maneuvering time for crane control},
author = {Khorshid, E.; Al-Fadhli,A.; Alghanim, K.; Baroon, J.},
journal = {Journal of Vibration and Control},
year = {2020},
institution = {Kuwait University, Kuwait; Public Authority for Applied Education and Training (PAAET), Kuwait},
abstract = {This study introduces a modified near-time–optimal rigid-body command that represents the fastest possible command profile based on using the full input capabilities of the system, considering rest-to-rest motion. The control objective is to have the shortest maneuvering time suitable for handling insensitive payloads. The rest-to-rest motion is divided into three stages. The first stage includes a quick response with maximum trolley acceleration. The second stage involves cruising at the maximum trolley velocity. The third stage provides deceleration, where both zero vibration and the zero vibration derivative are applied. The proposed shaper is simulated numerically for testing its performance. The theoretical findings were validated experimentally using a prototype crane. This study’s major finding demonstrates that the new technique succeeded in reducing the maneuvering time with zero vibration at the end of the motion. Moreover, the results show the insensitivity of the proposed shaper to variations in system parameters using a zero vibration derivative shaper. },
issn = {1077-5463},
keywords = {Single-mode system, command shaping, single pendulum, time-optimal rigid body, overhead crane},
language = {English},
publisher = {SAGE Publications}
}

This study introduces a modified near-time–optimal rigid-body command that represents the fastest possible command profile based on using the full input capabilities of the system, considering rest-to-rest motion. The control objective is to have the shortest maneuvering time suitable for handling insensitive payloads. The rest-to-rest motion is divided into three stages. The first stage includes a quick response with maximum trolley acceleration. The second stage involves cruising at the maximum trolley velocity. The third stage provides deceleration, where both zero vibration and the zero vibration derivative are applied. The proposed shaper is simulated numerically for testing its performance. The theoretical findings were validated experimentally using a prototype crane. This study’s major finding demonstrates that the new technique succeeded in reducing the maneuvering time with zero vibration at the end of the motion. Moreover, the results show the insensitivity of the proposed shaper to variations in system parameters using a zero vibration derivative shaper.

@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.

@conference{fallahinia_2020,
title = {Comparison of Constrained and Unconstrained Human Grasp Forces Using Fingernail Imaging and Visual Servoing},
author = {Fallahinia, N.; Mascaro, S.A.},
booktitle = {2020 IEEE International Conference on Robotics and Automation (ICRA)},
year = {2020},
institution = {University of Utah, USA},
abstract = {Fingernail imaging has been proven to be effective in prior works [1],[2] for estimating the 3D fingertip forces with a maximum RMS estimation error of 7%. In the current research, fingernail imaging is used to perform unconstrained grasp force measurement on multiple fingers to study human grasping. Moreover, two robotic arms with mounted cameras and a visual tracking system have been devised to keep the human fingers in the camera frame during the experiments. Experimental tests have been conducted for six human subjects under both constrained and unconstrained grasping conditions, and the results indicate a significant difference in force collaboration among the fingers between the two grasping conditions. Another interesting result according to the experiments is that in comparison to constrained grasping, unconstrained grasp forces are more evenly distributed over the fingers and there is less force variation (more steadiness) in each finger force. These results validate the importance of measuring grasp forces in an unconstrained manner in order to study how humans naturally grasp objects. },
language = {English},
publisher = {IEEE}
}

Fingernail imaging has been proven to be effective in prior works [1],[2] for estimating the 3D fingertip forces with a maximum RMS estimation error of 7%. In the current research, fingernail imaging is used to perform unconstrained grasp force measurement on multiple fingers to study human grasping. Moreover, two robotic arms with mounted cameras and a visual tracking system have been devised to keep the human fingers in the camera frame during the experiments. Experimental tests have been conducted for six human subjects under both constrained and unconstrained grasping conditions, and the results indicate a significant difference in force collaboration among the fingers between the two grasping conditions. Another interesting result according to the experiments is that in comparison to constrained grasping, unconstrained grasp forces are more evenly distributed over the fingers and there is less force variation (more steadiness) in each finger force. These results validate the importance of measuring grasp forces in an unconstrained manner in order to study how humans naturally grasp objects.

@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{zhang_2020,
title = {Compound Adaptive Fuzzy Quantized Control for Quadrotor and Its Experimental Verification},
author = {Zhang, X.; Wang, Y.; Zhu, G.; Chen, X.; Li, Z.; Wang, C.; Su, C.-Y.},
journal = {IEEE Transactions on Cybernetics},
year = {2020},
institution = {Northeast Electric Power University, China; Shibaura Institute of Technology, Japan; Northeastern University, China; Beihang University, China; Concordia University, Canada},
abstract = {This article aims to realize a precise position and attitude tracking control for the quadrotor using a proposed fuzzy approximator-based compound adaptive fuzzy quantized control scheme. In the control scheme, a quantized output-feedback control for position tracking and a state-feedback quantized control for attitude trajectory tracking are combined to deal with the underactuated and strong coupling problems of the quadrotor. The main contributions are: 1) the adaptive fuzzy quantized control is realized, then the strong nonlinearities caused by the quantizer are effectively mitigated, which implies that the control precision can be improved when a low communication rate is required in the real-time control system of quadrotor; 2) by applying the adaptive fuzzy dynamic surface control (DSC) technique to the underactuated quadrotor control system, the ``explosion of complexity'' problem in the backstepping method is overcome and the L∞ tracking performance is achieved with the proposed initializing technique inspired by Zhang et al. This guarantees that the attitude signals promptly converge to the desired trajectories, then the underactuated problem of the quadrotor is overcome by solving the designed adaptive fuzzy-quantized control equations; and 3) the experiments on the platform of the Quanser Qball-X4 quadrotor are conducted and the effectiveness of the proposed control scheme is validated. },
issn = {2168-2267},
keywords = {Adaptive quantized control, quadrotor, robust adaptive control},
language = {English},
publisher = {IEEE}
}

This article aims to realize a precise position and attitude tracking control for the quadrotor using a proposed fuzzy approximator-based compound adaptive fuzzy quantized control scheme. In the control scheme, a quantized output-feedback control for position tracking and a state-feedback quantized control for attitude trajectory tracking are combined to deal with the underactuated and strong coupling problems of the quadrotor. The main contributions are: 1) the adaptive fuzzy quantized control is realized, then the strong nonlinearities caused by the quantizer are effectively mitigated, which implies that the control precision can be improved when a low communication rate is required in the real-time control system of quadrotor; 2) by applying the adaptive fuzzy dynamic surface control (DSC) technique to the underactuated quadrotor control system, the ``explosion of complexity'' problem in the backstepping method is overcome and the L∞ tracking performance is achieved with the proposed initializing technique inspired by Zhang et al. This guarantees that the attitude signals promptly converge to the desired trajectories, then the underactuated problem of the quadrotor is overcome by solving the designed adaptive fuzzy-quantized control equations; and 3) the experiments on the platform of the Quanser Qball-X4 quadrotor are conducted and the effectiveness of the proposed control scheme is validated.

@article{singh2_2020,
title = {Condition Monitoring based Control using Wavelets and Machine Learning for Unmanned Surface Vehicles},
author = {Singh, R.; Bhushan, B.},
journal = {IEEE Transactions on Industrial Electronics},
year = {2020},
institution = {Delhi Technological University, India},
abstract = {This paper proposes the idea of fault detection and diagnosis for stable operation of unmanned surface vehicles (USVs). The idea of fault classification is achieved with the help of wavelet transforms and support vector machines, and the diagnosis is performed using a fuzzy controller. Initially, a brief idea of faults that effect the stable operation of USVs are identified through two degree of freedom ball balancer setup. The laboratory setup resembles the two translational motion of USVs like surge and sway. Further, the fault data is taken from plate angle, ball position and motor input voltage for developing a fault classification algorithm. The proposed algorithm depicted improved classification accuracy when compared with conventional methods. To accommodate the operation of the system as per the operating state, a wavelet based fuzzy controller is proposed. The proposed controller solves the problem of position tracking and balancing for ball and plate system with high precision, hence achieving the stable operation. },
issn = {0278-0046},
keywords = {Ball Balancer, Unmanned Surface Vehicles, Wavelet Transform, Support Vector Machine, Fault Classification, Wavelet Fuzzy Control},
language = {English},
publisher = {IEEE}
}

This paper proposes the idea of fault detection and diagnosis for stable operation of unmanned surface vehicles (USVs). The idea of fault classification is achieved with the help of wavelet transforms and support vector machines, and the diagnosis is performed using a fuzzy controller. Initially, a brief idea of faults that effect the stable operation of USVs are identified through two degree of freedom ball balancer setup. The laboratory setup resembles the two translational motion of USVs like surge and sway. Further, the fault data is taken from plate angle, ball position and motor input voltage for developing a fault classification algorithm. The proposed algorithm depicted improved classification accuracy when compared with conventional methods. To accommodate the operation of the system as per the operating state, a wavelet based fuzzy controller is proposed. The proposed controller solves the problem of position tracking and balancing for ball and plate system with high precision, hence achieving the stable operation.

@article{yildiz_2020,
title = {Constrained adaptive backstepping control of a semi‐active suspension considering suspension travel limits},
author = {Yildiz, A.S.; Sivrioglu, S.},
journal = {Asian Journal of Control},
year = {2020},
institution = {Gumushane University, Turkey; Gebze Technical University, Turkey},
abstract = {In this article, a new constrained adaptive backstepping control design is presented for vibration suppression of the quarter car suspension system equipped with a magnetorheological (MR) damper. In the controller design, parametric uncertainties related to suspension parameter and the semi‐active actuator are considered. Also, the proposed controller is investigated, along with the designed road profiles based on the International Standard Organization (ISO) 8608 classification and tested under different speed conditions. The effectiveness of the proposed controller has been validated through comparisons with uncontrolled, passive, and separately designed adaptive control cases. According to the simulation and experimental results, the proposed control approach improves ride comfort and ride safety without violating suspension rattle space constraint despite the uncertainties in suspension parameters. },
keywords = {Semi-active suspension, Barrier Lyapunov function, Backstepping controller, ISO 8608},
language = {English},
publisher = {John Wiley & Sons, Inc.}
}

In this article, a new constrained adaptive backstepping control design is presented for vibration suppression of the quarter car suspension system equipped with a magnetorheological (MR) damper. In the controller design, parametric uncertainties related to suspension parameter and the semi‐active actuator are considered. Also, the proposed controller is investigated, along with the designed road profiles based on the International Standard Organization (ISO) 8608 classification and tested under different speed conditions. The effectiveness of the proposed controller has been validated through comparisons with uncontrolled, passive, and separately designed adaptive control cases. According to the simulation and experimental results, the proposed control approach improves ride comfort and ride safety without violating suspension rattle space constraint despite the uncertainties in suspension parameters.

@article{zaare_2020,
title = {Continuous fuzzy nonsingular terminal sliding mode control of flexible joints robot manipulators based on nonlinear finite time observer in the presence of matched and mismatched uncertainties},
author = {Zaare, S.; Soltanpour, M.R.},
journal = {Journal of the Franklin Institute},
year = {2020},
institution = {Khatam Al-Anbiya University, Iran; Shahid Sattari Aeronautical University of Science and Technology, Iran},
abstract = {In the present study, a continuous fuzzy nonsingular terminal sliding mode control (CFNTSMC) was proposed based on the nonlinear finite-time observer (NFTO), in a voltage-based manner, to control the position of a serial-chain n-link flexible-joint electrically-driven robot manipulator in presence of matched and mismatched uncertainties with the electrical and mechanical equations. In order to design the controller sliding surface, a continuous function was proposed, which could make the concerned sliding surface zero at the beginning of the control process so that the proposed control had only the sliding phase. Afterward, an NFTO was adopted to facilitate access to the information of the state variables and matched and mismatched uncertainties. According to the Lyapunov stability theorems, the closed-loop control system had finite-time global asymptotically stability in presence of NFTO and existing uncertainties. Finally, a fuzzy-based control law was proposed to avoid chattering phenomena in the control input, whose computational complexity was extremely low. In order to evaluate the efficiency of the proposed control, multi-stage simulations were conducted for a 2-link flexible-joint serial robot manipulator. The mathematical evidence and simulation results confirmed the desired performance of the proposed CFNTSMC. },
keywords = {flexible joint robot manipulator, nonlinear finite time observer, nonsingular terminal sliding mode control, voltage-based, chattering, matched and mismatched uncertainties},
language = {English},
publisher = {The Franklin Institute}
}

In the present study, a continuous fuzzy nonsingular terminal sliding mode control (CFNTSMC) was proposed based on the nonlinear finite-time observer (NFTO), in a voltage-based manner, to control the position of a serial-chain n-link flexible-joint electrically-driven robot manipulator in presence of matched and mismatched uncertainties with the electrical and mechanical equations. In order to design the controller sliding surface, a continuous function was proposed, which could make the concerned sliding surface zero at the beginning of the control process so that the proposed control had only the sliding phase. Afterward, an NFTO was adopted to facilitate access to the information of the state variables and matched and mismatched uncertainties. According to the Lyapunov stability theorems, the closed-loop control system had finite-time global asymptotically stability in presence of NFTO and existing uncertainties. Finally, a fuzzy-based control law was proposed to avoid chattering phenomena in the control input, whose computational complexity was extremely low. In order to evaluate the efficiency of the proposed control, multi-stage simulations were conducted for a 2-link flexible-joint serial robot manipulator. The mathematical evidence and simulation results confirmed the desired performance of the proposed CFNTSMC.

@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{mehedi_2020,
title = {Controlling a Rotary Servo Cart System Using Robust Generalized Dynamic Inversion},
author = {Mehedi, I.M.; Ansari, U.; Al-Saggaf, U.M.; Bajodah, A.H.},
journal = {International Journal of Robotics and Automation},
year = {2020},
volume = {35},
number = {1},
institution = {King Abdulaziz University, Saudi Arabia},
abstract = {This paper presents the design and implementation of the Robust Generalized Dynamic Inversion (RGDI) based position and speed control of the rotary servo cart system. The RGDI control law comprises the two sub-parts, i.e., the equivalent control part and the robust control part. The equivalent control part represents the conventional form of the Generalized Dynamic Inversion control, in which dynamical constraints based on the deviation functions of angular position and its rate are formulated that encapsulates the control objectives. The control law is realized by inverting the constraint dynamics using dynamically scaled generalized inversion. The robust control part consists of an additional term based on the sliding mode principle that makes the control law robust with respect to the parametric fluctuation and outward disturbances while providing improved tracking performance. The closed-loop stability of RGDI control is ensured using the positive definite Lyapunov candidate function, which will guarantee globally practical stable tracking performance. Numerical simulations in Matlab/Simulink environment and the real-time experiments are performed on the Quanser’s SRV02 rotary servo cart platform. It exhibits better tracking performance through simulation and the experimental results. Robustness attributes to the proposed control law under the influence of the parametric uncertainties and external disturbances. },
keywords = {Generalized dynamic inversion, servo cart system, Lyapunov stability, sliding mode control, robust control},
language = {English},
publisher = {ACTA Press}
}

This paper presents the design and implementation of the Robust Generalized Dynamic Inversion (RGDI) based position and speed control of the rotary servo cart system. The RGDI control law comprises the two sub-parts, i.e., the equivalent control part and the robust control part. The equivalent control part represents the conventional form of the Generalized Dynamic Inversion control, in which dynamical constraints based on the deviation functions of angular position and its rate are formulated that encapsulates the control objectives. The control law is realized by inverting the constraint dynamics using dynamically scaled generalized inversion. The robust control part consists of an additional term based on the sliding mode principle that makes the control law robust with respect to the parametric fluctuation and outward disturbances while providing improved tracking performance. The closed-loop stability of RGDI control is ensured using the positive definite Lyapunov candidate function, which will guarantee globally practical stable tracking performance. Numerical simulations in Matlab/Simulink environment and the real-time experiments are performed on the Quanser’s SRV02 rotary servo cart platform. It exhibits better tracking performance through simulation and the experimental results. Robustness attributes to the proposed control law under the influence of the parametric uncertainties and external disturbances.

In this brief, we study the cooperative output regulation of linear multiagent systems under switching communication topology and exosystem detectability constraints, and applications to the synchronization of networked motors. Compared to similar works in the literature, in this brief, we consider the problem scenario in which none of the agents can estimate the exosystem states from their individual measurements on any of the switching configurations of the system. In other words, no agent in the system can solve the output regulation problem independently. By devising a distributed observer on the exosystem states, and under intuitive assumptions on the combined detectability of the multiagent system and joint connectivity of the information graphs, we are able to solve the problem by a distributed feedback protocol. Finally, the developed theoretical results are applied to the synchronization problem of a group of motors in a time-varying communication network.

@article{jonnalagadda_2020,
title = {Current cycle feedback iterative learning control for tracking control of magnetic levitation system},
author = {Jonnalagadda, V.K.; Elumalai, V.K.; Agrawal, S.},
journal = {Transactions of the Institute of Measurement and Control},
year = {2020},
month = {2},
volume = {42},
number = {3},
institution = {Vellore Institute of Technology, India},
abstract = {This paper presents the current cycle feedback iterative learning control (CCF-ILC) augmented with the modified proportional integral derivative (PID) controller to improve the trajectory tracking and robustness of magnetic levitation (maglev) system. Motivated by the need to enhance the point to point control of maglev technology, which is widely used in several industrial applications ranging from photolithography to vibration control, we present a novel CCF-ILC framework using plant inversion technique. Modulating the control signal based on the current tracking error, CCF-ILC reduces the dependency on accurate plant model and significantly improves the robustness of the closed loop system by synthesizing the causal filters to counteract the effect of model uncertainty. To assess the stability, we present a maximum singular value based criterion for asymptotic stability of linear iterative system controlled using CCF-ILC. In addition, we prove the monotonic convergence of output sequence in the neighbourhood of reference trajectory. Finally, the proposed control framework is experimentally validated on a benchmark magnetic levitation system through hardware in loop (HIL) testing. Experimental results substantiate that synthesizing CCF-ILC with the feedback controller can significantly improve the trajectory tracking and robustness characteristics of maglev system. },
keywords = {ILC, PIV, intelligent control, magnetic levitation system, command following},
language = {English},
publisher = {SAGE Publications}
}

This paper presents the current cycle feedback iterative learning control (CCF-ILC) augmented with the modified proportional integral derivative (PID) controller to improve the trajectory tracking and robustness of magnetic levitation (maglev) system. Motivated by the need to enhance the point to point control of maglev technology, which is widely used in several industrial applications ranging from photolithography to vibration control, we present a novel CCF-ILC framework using plant inversion technique. Modulating the control signal based on the current tracking error, CCF-ILC reduces the dependency on accurate plant model and significantly improves the robustness of the closed loop system by synthesizing the causal filters to counteract the effect of model uncertainty. To assess the stability, we present a maximum singular value based criterion for asymptotic stability of linear iterative system controlled using CCF-ILC. In addition, we prove the monotonic convergence of output sequence in the neighbourhood of reference trajectory. Finally, the proposed control framework is experimentally validated on a benchmark magnetic levitation system through hardware in loop (HIL) testing. Experimental results substantiate that synthesizing CCF-ILC with the feedback controller can significantly improve the trajectory tracking and robustness characteristics of maglev system.

@article{yousfi_2020,
title = {Decentralized nonlinear robust control for multi-variable systems: Application to a 2 DoF labora-tory helicopter},
author = {Yousfi, M.; Ben Njima, C.; Garna, T.},
journal = {Acta Polytechnica Hungarica},
year = {2020},
volume = {17},
number = {5},
institution = {University of Monastir, Tunisia},
abstract = {In this paper a decentralized nonlinear robust control (DNRC) using loop shaping design procedure (LSDP) and gain scheduling (GS) technique is developed for MIMO (multiple-input/multiple-output) systems. The nonlinear system is linearized in several equilibrium points and for each of these latter a decentralized robust controller is calculated, using a proposed LSDP based on RGA (Relative Gain Array) theory, to regulate the system around the equilibrium point. To do this. The use of RGA theory is exploited to define the structure of the weighting controller of the LSDP with the most effective input/output pairing. Then, for each equilibrium point a full-order robust controller is calculated using LSDP, which configuration is simplified exploiting the RGA theory by proposing a selection matrix to deduce a simplified final robust controller. The overall control system is obtained by GS technique from switching between local simplified final robust controllers according to the scheduling parameter’s (SP) value. The proposed algorithm of control is validated on the AERO system of Quanser. },
keywords = {Quanser’s AERO; MIMO nonlinear systems; RGA theory; robustness; decentralized control; gain scheduling},
language = {English}
}

In this paper a decentralized nonlinear robust control (DNRC) using loop shaping design procedure (LSDP) and gain scheduling (GS) technique is developed for MIMO (multiple-input/multiple-output) systems. The nonlinear system is linearized in several equilibrium points and for each of these latter a decentralized robust controller is calculated, using a proposed LSDP based on RGA (Relative Gain Array) theory, to regulate the system around the equilibrium point. To do this. The use of RGA theory is exploited to define the structure of the weighting controller of the LSDP with the most effective input/output pairing. Then, for each equilibrium point a full-order robust controller is calculated using LSDP, which configuration is simplified exploiting the RGA theory by proposing a selection matrix to deduce a simplified final robust controller. The overall control system is obtained by GS technique from switching between local simplified final robust controllers according to the scheduling parameter’s (SP) value. The proposed algorithm of control is validated on the AERO system of Quanser.

@article{huang_2020,
title = {Demonstration of a model-free backstepping control on a 2-DOF laboratory helicopter},
author = {Huang, J.-W.; Fan, Y.; Xin, Y.; Qin, Z.-C.},
journal = {International Journal of Dynamics and Control},
year = {2020},
institution = {Beijing University of Chemical Technology, China; Tianjin University of Technology, China; Shandong University of Technology, China},
abstract = {A model-free backstepping (MFBS) control design method is proposed to deal with problems of model uncertainties, dynamic coupling and time-varying coefficients of nonlinear dynamic systems. A popular benchmark system of 2-DOF laboratory helicopter is used to demonstrate and validate the theoretical development. The MFBS method makes use of techniques including the normal form, model estimation and cancellation, and control coefficient substitution. The model estimation is based on the input and output data only. The model free backstepping control is designed with the Lyapunov method and its stability is proven. The equivalence of the control designed with the control coefficient substitution to that with the full knowledge of the model is discussed. Simulation and experimental results are presented to show the effectiveness of the MFBS control design in comparison with a common LQR control. },
keywords = {Model-free backstepping (MFBS), 2-DOF helicopter, Coupled dynamics, Time-varying system},
language = {English},
publisher = {Springer Nature Switzerland}
}

A model-free backstepping (MFBS) control design method is proposed to deal with problems of model uncertainties, dynamic coupling and time-varying coefficients of nonlinear dynamic systems. A popular benchmark system of 2-DOF laboratory helicopter is used to demonstrate and validate the theoretical development. The MFBS method makes use of techniques including the normal form, model estimation and cancellation, and control coefficient substitution. The model estimation is based on the input and output data only. The model free backstepping control is designed with the Lyapunov method and its stability is proven. The equivalence of the control designed with the control coefficient substitution to that with the full knowledge of the model is discussed. Simulation and experimental results are presented to show the effectiveness of the MFBS control design in comparison with a common LQR control.

@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{sistla_2020,
title = {Design and performance comparison of interconnection and damping assignment passivity-based control for vibration suppression in active suspension systems},
author = {Sistla, P.; Figarado, S.; Chemmangat, K.; Manjarekar, S.; Valappil, G.K.},
journal = {Journal of Vibration and Control},
year = {2020},
institution = {National Institute of Technology Karnataka, India; Indian Institute of Technology Goa, India; BITS Pilani, India},
abstract = {This study presents the design of interconnection and damping assignment passivity-based control for active suspension systems. It is well known that interconnection and damping assignment passivity-based control’s design methodology is based on the physical properties of the system where the kinetic and potential energy profiles are shaped, and asymptotic stability is achieved by damping injection. Based on the choice of control variables, special cases of the control law are derived, and tuning of the control law with the physical meaning of the variables is demonstrated along with their simulation results. The proposed control law is experimentally validated on a scaled model of a quarter-car active suspension system with different road profiles, varying load conditions, and noise and delay in the sensor measurements and actuator respectively. The results are compared with that of an uncontrolled system with linear quadratic regulator and sliding mode control. },
issn = {1077-5463},
keywords = {Interconnection and damping assignment passivity based control, passivity, port-Hamiltonian, active suspension system, active damping injection, energy-shaping, quarter-car test-rig},
language = {English},
publisher = {SAGE Publications}
}

This study presents the design of interconnection and damping assignment passivity-based control for active suspension systems. It is well known that interconnection and damping assignment passivity-based control’s design methodology is based on the physical properties of the system where the kinetic and potential energy profiles are shaped, and asymptotic stability is achieved by damping injection. Based on the choice of control variables, special cases of the control law are derived, and tuning of the control law with the physical meaning of the variables is demonstrated along with their simulation results. The proposed control law is experimentally validated on a scaled model of a quarter-car active suspension system with different road profiles, varying load conditions, and noise and delay in the sensor measurements and actuator respectively. The results are compared with that of an uncontrolled system with linear quadratic regulator and sliding mode control.

@article{shendge_2020,
title = {Disturbance observer based controller under noisy measurement for tracking of nDOF uncertain mismatched nonlinear},
author = {Chaudhari, S.; Shendge, P.D.; Phadke, S.B.},
journal = {IEEE/ASME Transactions on Mechatronics},
year = {2020},
institution = {College of Engineering Pune, India},
abstract = {This paper analyzes a recent result on tracking control of nonlinear, mismatched interconnected systems and points out its drawback with regard to noise amplification that originates from numerical differentiation of tracking error. One of the objectives of the paper is to propose two remedial measures and show their improvements by analysis for performance and stability. In the first remedy, the derivative of the tracking error is obtained without recourse to numerical differentiation while the second remedy offers a solution that does not need the derivative of the tracking error. Another objective of the paper is to propose an extension to decentralized sliding mode control of interconnected systems. Unlike other published results, the extension can assure local sliding in each subsystem. The proposed remedies are validated by numerical simulation, and experimentation on a laboratory coupled tank system. A discussion with a generalized extended state observer (GESO) based method and comparative simulations with other well known methods are carried out. },
keywords = {disturbance observer, sliding mode control, measurement noise, mismatched nonlinear systems, decentralized control},
language = {English},
publisher = {IEEE}
}

This paper analyzes a recent result on tracking control of nonlinear, mismatched interconnected systems and points out its drawback with regard to noise amplification that originates from numerical differentiation of tracking error. One of the objectives of the paper is to propose two remedial measures and show their improvements by analysis for performance and stability. In the first remedy, the derivative of the tracking error is obtained without recourse to numerical differentiation while the second remedy offers a solution that does not need the derivative of the tracking error. Another objective of the paper is to propose an extension to decentralized sliding mode control of interconnected systems. Unlike other published results, the extension can assure local sliding in each subsystem. The proposed remedies are validated by numerical simulation, and experimentation on a laboratory coupled tank system. A discussion with a generalized extended state observer (GESO) based method and comparative simulations with other well known methods are carried out.

@article{wang4_2020,
title = {Dynamic Control of Multi-Section Three-Dimensional Continuum Manipulators Based on Virtual Discrete-Jointed Robot Models},
author = {Wang, C.; Frazelle, C.G.; Wagner, J.R.; Walker, I.},
journal = {IEEE/ASME Transactions on Mechatronics},
year = {2020},
institution = {Clemson University, USA},
abstract = {Despite the rise of development in continuum manipulator technology and application, a model-based feedback closed-loop control appropriate for continuum robot designs has remained a significant challenge. Complicated by the soft and flexible nature of the manipulator body, control of continuum structures with infinite dimensions proves to be difficult due to their complex dynamics. In this paper, a novel strategy is designed for trajectory control of a multi-section continuum robot in three-dimensional (3D) space to achieve accurate orientation, curvature, and section length tracking. The formulation connects the continuum manipulator dynamic behavior to a virtual discrete-jointed robot whose degrees of freedom are directly mapped to those of a continuum robot section under the hypothesis of constant curvature. Based on this connection, a computed torque control architecture is developed for the virtual robot, for which kinematics and dynamic equations are constructed and exploited, with appropriate transformations developed for implementation on the continuum robot. The control algorithm is implemented on a six Degree-of-Freedom two-section OctArm continuum manipulator. Experiments show that the proposed method could manage simultaneous extension/contraction, bending, and torsion actions on multi-section continuum robots with decent performance (arc length and curvature error of ±4mm and ±0.35 m −1 ). The designed dynamic controller can reduce the curvature tracking error and rise time by up to 48.1% and 94.8% compared to the traditional PID controller during two-section maneuvers. },
issn = {1083-4435},
keywords = {Continuum Robot, Forward Kinematics, Inverse Kinematics, Manipulator Dynamics, Motion Control},
language = {English},
publisher = {IEEE}
}

Despite the rise of development in continuum manipulator technology and application, a model-based feedback closed-loop control appropriate for continuum robot designs has remained a significant challenge. Complicated by the soft and flexible nature of the manipulator body, control of continuum structures with infinite dimensions proves to be difficult due to their complex dynamics. In this paper, a novel strategy is designed for trajectory control of a multi-section continuum robot in three-dimensional (3D) space to achieve accurate orientation, curvature, and section length tracking. The formulation connects the continuum manipulator dynamic behavior to a virtual discrete-jointed robot whose degrees of freedom are directly mapped to those of a continuum robot section under the hypothesis of constant curvature. Based on this connection, a computed torque control architecture is developed for the virtual robot, for which kinematics and dynamic equations are constructed and exploited, with appropriate transformations developed for implementation on the continuum robot. The control algorithm is implemented on a six Degree-of-Freedom two-section OctArm continuum manipulator. Experiments show that the proposed method could manage simultaneous extension/contraction, bending, and torsion actions on multi-section continuum robots with decent performance (arc length and curvature error of ±4mm and ±0.35 m −1 ). The designed dynamic controller can reduce the curvature tracking error and rise time by up to 48.1% and 94.8% compared to the traditional PID controller during two-section maneuvers.