@article{leylaz_2021,
title = {An Optimal Model Identification Algorithm of Nonlinear Dynamical Systems With the Algebraic Method},
author = {Leylaz, G.; Ma, S.; Sun, J.-Q.},
journal = {Journal of Vibration and Acoustics},
year = {2021},
month = {04},
volume = {143},
number = {2},
institution = {University of California, Merced, USA},
abstract = {This article proposes a nonparametric system identification technique to discover the governing equation of nonlinear dynamic systems with the focus on practical aspects. The algorithm builds on Brunton’s work in 2016 and combines the sparse regression with an algebraic calculus to estimate the required derivatives of the measurements. This reduces the required derivative data for the system identification. Furthermore, we make use of the concepts of K-fold cross validation from machine learning and information criteria for model selection. This allows the system identification with less measurements than the typically required data for the sparse regression. The result is an optimal model for the underlining system of the data with a minimum number of terms. The proposed nonparametric system identification method is applicable for multiple-input–multiple-output systems. Two examples are presented to demonstrate the proposed method. The first one makes use of the simulated data of a nonlinear oscillator to show the effectiveness and accuracy of the proposed method. The second example is a nonlinear rotary flexible beam. Experimental responses of the beam are used to identify the underlining model. The Coulomb friction in the servo motor together with other nonlinear terms of the system variables are found to be important components of the model. These are, otherwise, not available in the theoretical linear model of the system. },
issn = {1048-9002},
keywords = {data-driven modeling, algebraic differential estimation, sparse regression, system identification, nonlinear dynamics, flexible manipulator, dynamics, nonlinear vibration, system identification},
language = {English},
publisher = {ASME}
}

This article proposes a nonparametric system identification technique to discover the governing equation of nonlinear dynamic systems with the focus on practical aspects. The algorithm builds on Brunton’s work in 2016 and combines the sparse regression with an algebraic calculus to estimate the required derivatives of the measurements. This reduces the required derivative data for the system identification. Furthermore, we make use of the concepts of K-fold cross validation from machine learning and information criteria for model selection. This allows the system identification with less measurements than the typically required data for the sparse regression. The result is an optimal model for the underlining system of the data with a minimum number of terms. The proposed nonparametric system identification method is applicable for multiple-input–multiple-output systems. Two examples are presented to demonstrate the proposed method. The first one makes use of the simulated data of a nonlinear oscillator to show the effectiveness and accuracy of the proposed method. The second example is a nonlinear rotary flexible beam. Experimental responses of the beam are used to identify the underlining model. The Coulomb friction in the servo motor together with other nonlinear terms of the system variables are found to be important components of the model. These are, otherwise, not available in the theoretical linear model of the system.

@article{kana_2020,
title = {Human–Robot co-manipulation during surface tooling: A general framework based on impedance control, haptic rendering and discrete geometry},
author = {Kana, S.; Campolo, D. Tee, K.-P.},
journal = {Robotics and Computer-Integrated Manufacturing},
year = {2021},
month = {02},
volume = {67},
institution = {Nanyang Technological University, Singapore Institute for Infocomm Research, Singapore},
abstract = {Despite the advancements in machine learning and artificial intelligence, there are many tooling tasks with cognitive aspects that are rather challenging for robots to handle in full autonomy, thus still requiring a certain degree of interaction with a human operator. In this paper, we propose a theoretical framework for both planning and execution of robot-surface contact tasks whereby interaction with a human operator can be accommodated to a variable degree. The starting point is the geometry of surface, which we assume known and available in a discretized format, e.g. through scanning technologies. To allow for realtime computation, rather than interacting with thousands of vertices, the robot only interacts with a single proxy, i.e. a massless virtual object constrained to ‘live on’ the surface and subject to first order viscous dynamics. The proxy and an impedance-controlled robot are then connected through tuneable and possibly viscoelastic coupling, i.e. (virtual) springs and dampers. On the one hand, the proxy slides along discrete geodesics of the surface in response to both viscoelastic coupling with the robot and to a possible external force (a virtual force which can be used to induce autonomous behaviours). On the other hand, the robot is free to move in 3D in reaction to the same viscoelastic coupling as well as to a possible external force, which includes an actual force exerted by a human operator. The proposed approach is multi-objective in the sense that different operational (autonomous/collaborative) and interactive (for contact/non-contact tasks) modalities can be realized by simply modulating the viscoelastic coupling as well as virtual and physical external forces. We believe that our proposed framework might lead to a more intuitive interfacing to robot programming, as opposed to standard coding. To this end, we also present numerical and experimental studies demonstrating path planning as well as autonomous and collaborative interaction for contact tasks with a free-form surface. },
keywords = {Robot-surface interaction, Impedance control, Human–Robot collaboration, Virtual proxy, Virtual fixtures, Triangular mesh model, Geodesics},
language = {English},
publisher = {Elsevier B.V.}
}

Despite the advancements in machine learning and artificial intelligence, there are many tooling tasks with cognitive aspects that are rather challenging for robots to handle in full autonomy, thus still requiring a certain degree of interaction with a human operator. In this paper, we propose a theoretical framework for both planning and execution of robot-surface contact tasks whereby interaction with a human operator can be accommodated to a variable degree.

The starting point is the geometry of surface, which we assume known and available in a discretized format, e.g. through scanning technologies. To allow for realtime computation, rather than interacting with thousands of vertices, the robot only interacts with a single proxy, i.e. a massless virtual object constrained to ‘live on’ the surface and subject to first order viscous dynamics. The proxy and an impedance-controlled robot are then connected through tuneable and possibly viscoelastic coupling, i.e. (virtual) springs and dampers. On the one hand, the proxy slides along discrete geodesics of the surface in response to both viscoelastic coupling with the robot and to a possible external force (a virtual force which can be used to induce autonomous behaviours). On the other hand, the robot is free to move in 3D in reaction to the same viscoelastic coupling as well as to a possible external force, which includes an actual force exerted by a human operator. The proposed approach is multi-objective in the sense that different operational (autonomous/collaborative) and interactive (for contact/non-contact tasks) modalities can be realized by simply modulating the viscoelastic coupling as well as virtual and physical external forces. We believe that our proposed framework might lead to a more intuitive interfacing to robot programming, as opposed to standard coding. To this end, we also present numerical and experimental studies demonstrating path planning as well as autonomous and collaborative interaction for contact tasks with a free-form surface.

@article{zhu_2021,
title = {Robust attitude control of a 3-DOF helicopter considering actuator saturation},
author = {Zhu, X.; Li, D.},
journal = {Mechanical Systems and Signal Processing},
year = {2021},
month = {02},
volume = {149},
institution = {Southeast University, China Shanghai Maritime University, China},
abstract = {This paper presents a comparative research of robust attitude control for helicopter system. Considering the actuator saturation problem in the controller design process, two control approaches are mainly investigated. A mixed H∞ performance and linear quadratic regulator (LQR) based robust controller is proposed as the first approach, in which the control input is indirectly constrained in the cost function. While a constrained H∞ performance based robust attitude controller is developed as the other approach, in which the actuator saturation problem is directly considered in the dynamical model as well as following controller gain calculation. Based on Lyapunov stability theory, sufficient conditions in terms of linear matrix inequalities (LMI) are given. By using Quanser’s 3-DOF laboratory helicopter platform, comparative simulation and experimental tests are conducted. Payload variation and wind disturbance are all considered in the experimental test. And the results demonstrate the effectiveness as well as different performance of proposed robust attitude controllers. },
keywords = {Robust attitude control 3-DOF helicopter, Actuator saturation, Comparative experimental tests},
language = {English},
publisher = {Elsevier Ltd.}
}

This paper presents a comparative research of robust attitude control for helicopter system. Considering the actuator saturation problem in the controller design process, two control approaches are mainly investigated. A mixed H∞ performance and linear quadratic regulator (LQR) based robust controller is proposed as the first approach, in which the control input is indirectly constrained in the cost function. While a constrained H∞ performance based robust attitude controller is developed as the other approach, in which the actuator saturation problem is directly considered in the dynamical model as well as following controller gain calculation. Based on Lyapunov stability theory, sufficient conditions in terms of linear matrix inequalities (LMI) are given. By using Quanser’s 3-DOF laboratory helicopter platform, comparative simulation and experimental tests are conducted. Payload variation and wind disturbance are all considered in the experimental test. And the results demonstrate the effectiveness as well as different performance of proposed robust attitude controllers.

@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.

@conference{ortega-vidal_2020,
title = {A comparison between optimal LQR control and LQR predictive control of a planar robot of 2DOF},
author = {Ortega–Vidal, A.; Salazar–Vasquez, F.; Rojas–Moren, A.},
booktitle = {2020 IEEE XXVII International Conference on Electronics, Electrical Engineering and Computing (INTERCON)},
year = {2020},
institution = {Universidad de Ingenieria y Tecnologia (UTEC), Peru},
abstract = {This work employs a LQR (Linear Quadratic Regulator) predictive control as well as a LQR controller to control the angular servomotor positions of a planar robot of 2DOF (2 Degrees of Freedom). The goal of such a pantograph type robot is to manipulate the X–Y positions of a 4–bar linkage end effector using two rotary servo base units connected to two revolute joints. Three unactuated revolute joints complete the five links of the robot. Experimental results demonstrate that the LQR predictive controller performs better than the LQR controller, because the former controller is able to diminish the steady–state error between the Cartesian coordinates with respect to the desired coordinates. },
keywords = {LQR controller, LQR predictive controller, inverse kinematics, direct kinematics, planar robot of 2DOF},
language = {English},
publisher = {IEEE},
isbn = {978-1-7281-9378-6}
}

This work employs a LQR (Linear Quadratic Regulator) predictive control as well as a LQR controller to control the angular servomotor positions of a planar robot of 2DOF (2 Degrees of Freedom). The goal of such a pantograph type robot is to manipulate the X–Y positions of a 4–bar linkage end effector using two rotary servo base units connected to two revolute joints. Three unactuated revolute joints complete the five links of the robot. Experimental results demonstrate that the LQR predictive controller performs better than the LQR controller, because the former controller is able to diminish the steady–state error between the Cartesian coordinates with respect to the desired coordinates.

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.

@conference{dhaybi_2020,
title = {Accurate Real-time Estimation of the Inertia Tensor of Package Delivery Quadrotors},
author = {Dhaybi, M.; Daher, N.},
booktitle = {2020 American Control Conference},
year = {2020},
institution = {American University of Beirut, Lebanon},
abstract = {The need for quadrotors to provide grasping and payload carrying abilities is ever-growing in several industries. Additional payloads attached to a quadrotor alter its dynamics and eventually affect its control system’s performance. In this work, an accurate real-time estimation of the varying mass and inertia tensor elements of a quadrotor carrying a variable payload is proposed. Parameter estimation is performed via a recursive least squares algorithm that is implemented on the quadrotor’s dynamic model using proper input-output data. Covariance resetting is integrated into the algorithm to increase the convergence rate and accuracy of the obtained estimates. The vertical motion is used to estimate the mass of the system, whereas the rotational motions around the x−, y− , and z − axes are used to identify the elements of the 3x3 inertia tensor matrix. The experiment is designed such that a persistently exciting input is generated to guarantee the convergence of the parameter estimates towards their true values. The proposed identification scheme is validated in numerical simulations and experimentation on a physical quadrotor, the Quanser QBall-2. The obtained results demonstrate the accuracy and convergence rate of the designed estimator, paving the way in front of its integration into an adaptive control system. },
issn = {0743-1619 },
keywords = {Tensile stress, Mathematical model, Estimation, Payloads, Adaptation models, Numerical models, Linear regression},
language = {English},
publisher = {IEEE},
isbn = {978-1-5386-8267-8}
}

The need for quadrotors to provide grasping and payload carrying abilities is ever-growing in several industries. Additional payloads attached to a quadrotor alter its dynamics and eventually affect its control system’s performance. In this work, an accurate real-time estimation of the varying mass and inertia tensor elements of a quadrotor carrying a variable payload is proposed. Parameter estimation is performed via a recursive least squares algorithm that is implemented on the quadrotor’s dynamic model using proper input-output data. Covariance resetting is integrated into the algorithm to increase the convergence rate and accuracy of the obtained estimates. The vertical motion is used to estimate the mass of the system, whereas the rotational motions around the x−, y− , and z − axes are used to identify the elements of the 3x3 inertia tensor matrix. The experiment is designed such that a persistently exciting input is generated to guarantee the convergence of the parameter estimates towards their true values. The proposed identification scheme is validated in numerical simulations and experimentation on a physical quadrotor, the Quanser QBall-2. The obtained results demonstrate the accuracy and convergence rate of the designed estimator, paving the way in front of its integration into an adaptive control system.

@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{larbaoui_2020,
title = {Adapting Hands-on Laboratory’s Materials and Embedded Systems from Local Use to Remote Experimenting through Internet},
author = {Larbaoui, Y.; Naddami, A.; Fahli, A.},
journal = {International Journal of Innovative Science and Research Technology},
year = {2020},
month = {07},
volume = {5},
number = {7},
institution = {University Hassan 1 Settat, Morocco},
abstract = {This paper presents the work of adapting hands-on laboratory’s materials of NI Elvis and Quanser from in-poste exploit to online access and remote exploit for online experimenting, after analyzing different aspects of adapting any hands-on laboratory’s material of in-place experimenting to remote exploit. This paper presents the work of developing a software multiplexing technique, and other techniques, to multiplex between different software codes and programs, in order to control different types of experiments in electronic of energy while using and sharing the same physical components and materials nearly simultaneously. In addition, this paper presents the work of creating web client interfaces; to use those embedded systems of NI Elvis and Quanser and their deployed experiments through the internet while relying on an e-learning platform of our remote lab to support their remote access. The principal advantage of conducted adaptations is sharing the same hardware and software resources between different experiments at the same time, while exploiting them locally and through the internet by multiusers. },
issn = {2456-2165},
keywords = {Circuits experimenting; e-learning; embedded systems; experiments switching; software switching; remote experiments},
language = {English}
}

This paper presents the work of adapting hands-on laboratory’s materials of NI Elvis and Quanser from in-poste exploit to online access and remote exploit for online experimenting, after analyzing different aspects of adapting any hands-on laboratory’s material of in-place experimenting to remote exploit. This paper presents the work of developing a software multiplexing technique, and other techniques, to multiplex between different software codes and programs, in order to control different types of experiments in electronic of energy while using and sharing the same physical components and materials nearly simultaneously. In addition, this paper presents the work of creating web client interfaces; to use those embedded systems of NI Elvis and Quanser and their deployed experiments through the internet while relying on an e-learning platform of our remote lab to support their remote access. The principal advantage of conducted adaptations is sharing the same hardware and software resources between different experiments at the same time, while exploiting them locally and through the internet by multiusers.

@article{wang5_2020,
title = {Adaptive Fast Smooth Second-Order Sliding Mode Control for Attitude Tracking of a 3-DOF Helicopter},
author = {Wang, X.; Li, Z.; He, Z.; Gao, H.},
journal = {arXiv},
year = {2020},
institution = {Harbin Institute of Technology, China},
abstract = {This paper presents a novel adaptive fast smooth second-order sliding mode control for the attitude tracking of the three degree-of-freedom (3-DOF) helicopter system with lumped disturbances. Combining with a non-singular integral sliding mode surface, we propose a novel adaptive fast smooth second-order sliding mode control method to enable elevation and pitch angles to track given desired trajectories respectively with the features of non-singularity, adaptation to disturbances, chattering suppression and fast finite-time convergence. In addition, a novel adaptive-gain smooth second-order sliding mode observer is proposed to compensate time-varying lumped disturbances with the smoother output compared with the adaptive-gain second-order sliding mode observer. The fast finite-time convergence of the closed-loop system with constant disturbances and the fast finite-time uniformly ultimately boundedness of the closed-loop system with the time-varying lumped disturbances are proved with the finite-time Lyapunov stability theory. Finally, the effectiveness and superiority of the proposed control methods are verified by comparative simulation experiments. },
keywords = {Adaptive fast smooth second-order sliding mode control (AFSSOSMC), adaptive-gain smooth secondorder sliding mode observer (ASSOSMO), 3-DOF helicopter},
language = {English}
}

This paper presents a novel adaptive fast smooth second-order sliding mode control for the attitude tracking of the three degree-of-freedom (3-DOF) helicopter system with lumped disturbances. Combining with a non-singular integral sliding mode surface, we propose a novel adaptive fast smooth second-order sliding mode control method to enable elevation and pitch angles to track given desired trajectories respectively with the features of non-singularity, adaptation to disturbances, chattering suppression and fast finite-time convergence. In addition, a novel adaptive-gain smooth second-order sliding mode observer is proposed to compensate time-varying lumped disturbances with the smoother output compared with the adaptive-gain second-order sliding mode observer. The fast finite-time convergence of the closed-loop system with constant disturbances and the fast finite-time uniformly ultimately boundedness of the closed-loop system with the time-varying lumped disturbances are proved with the finite-time Lyapunov stability theory. Finally, the effectiveness and superiority of the proposed control methods are verified by comparative simulation experiments.

@conference{wang6_2020,
title = {Adaptive Fault-Tolerant Control of a Quadrotor Helicopter Based on Sliding Mode Control and Radial Basis Function Neural Network},
author = {Wang, B.; Zhang, W.; Zhang, L.; Zhang, Y.},
booktitle = {2020 International Conference on Unmanned Aircraft Systems (ICUAS)},
year = {2020},
institution = {Northwestern Polytechnical University, China; China Aeronautical Radio Electronics Research Institute, China; Concordia University, China},
abstract = {In this paper, an adaptive fault-tolerant control strategy is proposed for a quadrotor helicopter in the presence of actuator faults and model uncertainties by integrating sliding mode control and radial basis function neural network. By assuming knowledge of the bounds on external disturbances, a baseline sliding mode control is first designed to achieve the desired system tracking performance and retain insensitive to disturbances. Then, regarding actuator faults and model uncertainties of the quadrotor helicopter, neural adaptive control schemes are constructed and incorporated into the baseline sliding mode control to deal with them. Finally, a series of simulation tests are conducted to validate the effectiveness of the proposed control strategy where a quadrotor helicopter is subject to inertial moment variations and different level of actuator faults. The capability of the proposed control strategy is confirmed and verified by the demonstrated results. },
issn = {2373-6720 },
keywords = {Helicopters, Actuators, Uncertainty, Propellers, Adaptation models, Torque, Sliding mode control},
language = {English},
publisher = {IEEE},
isbn = {978-1-7281-4279-1}
}

In this paper, an adaptive fault-tolerant control strategy is proposed for a quadrotor helicopter in the presence of actuator faults and model uncertainties by integrating sliding mode control and radial basis function neural network. By assuming knowledge of the bounds on external disturbances, a baseline sliding mode control is first designed to achieve the desired system tracking performance and retain insensitive to disturbances. Then, regarding actuator faults and model uncertainties of the quadrotor helicopter, neural adaptive control schemes are constructed and incorporated into the baseline sliding mode control to deal with them. Finally, a series of simulation tests are conducted to validate the effectiveness of the proposed control strategy where a quadrotor helicopter is subject to inertial moment variations and different level of actuator faults. The capability of the proposed control strategy is confirmed and verified by the demonstrated results.

@article{na_2020,
title = {Adaptive Identifier-Critic-Based Optimal Tracking Control for Nonlinear Systems With Experimental Validation},
author = {Na, J.; Zhao, J.; Lv, Y.; Zhang, K.},
journal = {IEEE Transactions on Systems, Man, and Cybernetics: Systems},
year = {2020},
institution = {Kunming University of Science and Technology, China; Beijing Institute of Technology, China; U.K. Atomic Energy Authority, UK},
abstract = {This article presents and practically validates an identifier-critic-based approximate dynamic programming (ADP) method to online address the optimal tracking control problem for nonlinear continuous-time unknown systems. The imposed assumption on precisely known system dynamics is obviated via a neural network (NN) identifier. A static control is first adopted to retain the steady-state tracking response, while an optimal control derived via the ADP method is proposed to regulate the tracking error by minimizing a cost function. A critic NN is then trained online to obtain the solution of the associated Hamilton-Jacobi-Bellman (HJB) equation. The learning of the identifier NN and critic NN is performed online simultaneously by tailoring a novel adaptation method, which can guarantee the convergence of the estimated NN weights. Consequently, the critic NN can be used to construct the optimal control policy directly, such that the actor NN used in the previous ADP schemes is avoided. Simulations are performed to verify the suggested control, and experiments on a helicopter plant are carried out to show its feasibility and improved control response. },
issn = {2168-2216 },
keywords = {Adaptive dynamic programming, adaptive control, neural network (NN), optimal control},
language = {English}
}

This article presents and practically validates an identifier-critic-based approximate dynamic programming (ADP) method to online address the optimal tracking control problem for nonlinear continuous-time unknown systems. The imposed assumption on precisely known system dynamics is obviated via a neural network (NN) identifier. A static control is first adopted to retain the steady-state tracking response, while an optimal control derived via the ADP method is proposed to regulate the tracking error by minimizing a cost function. A critic NN is then trained online to obtain the solution of the associated Hamilton-Jacobi-Bellman (HJB) equation. The learning of the identifier NN and critic NN is performed online simultaneously by tailoring a novel adaptation method, which can guarantee the convergence of the estimated NN weights. Consequently, the critic NN can be used to construct the optimal control policy directly, such that the actor NN used in the previous ADP schemes is avoided. Simulations are performed to verify the suggested control, and experiments on a helicopter plant are carried out to show its feasibility and improved control response.

@article{mohamed_2020,
title = {Adaptive position control of a cart moved by a DC motor using integral controller tuned by Jaya optimization with Balloon effect},
author = {Mohamed, T.H.; Mohamed Alamin, M.A.; Hassan, A.M.},
journal = {Computers & Electrical Engineering},
year = {2020},
month = {10},
volume = {87},
institution = {Aswan University, Egypt; Egyptian Hydro-Power Generation Company, Egypt; Arab Academy for Science, Technology & Maritime Transport, Egypt},
abstract = {This study suggests an adaptive linear position control of a car moved by an armature-controlled DC motor. In this study, Jaya optimization algorithm supported by Balloon effect (BE) is used to tune the parameters of the controller of the car position. BE is introduced to improve response of the classical Jaya algorithm in face of the external disturbance and system parameters changes. In the suggested technique, an objective function (OF) of the modified Jaya depends on the updated values of the controller gains and identified value of the motor open loop transfer function. System with the suggested controller has been evaluated in case of step load disturbance and motor parameters changes. Simulation and experimental results supported that suggested adaptive controller using modified Jaya improved the total system performance at moments of load disturbance and uncertainties of system parameters. },
keywords = {Position control, Jaya algorithm, Balloon effect, Optimization},
language = {English},
publisher = {Elsevier Ltd.}
}

This study suggests an adaptive linear position control of a car moved by an armature-controlled DC motor. In this study, Jaya optimization algorithm supported by Balloon effect (BE) is used to tune the parameters of the controller of the car position. BE is introduced to improve response of the classical Jaya algorithm in face of the external disturbance and system parameters changes. In the suggested technique, an objective function (OF) of the modified Jaya depends on the updated values of the controller gains and identified value of the motor open loop transfer function. System with the suggested controller has been evaluated in case of step load disturbance and motor parameters changes. Simulation and experimental results supported that suggested adaptive controller using modified Jaya improved the total system performance at moments of load disturbance and uncertainties of system parameters.

@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{garmsiri_2020,
title = {Admittance-Controlled Teleoperation of a Pneumatic Actuator: Implementation and Stability Analysis},
author = {Garmsiri, N.; Sun, Y.; Sekhavat, P.; Yang, C.X.; Sepehri, N.},
journal = {Actuators},
year = {2020},
volume = {9},
number = {4},
institution = {University of Manitoba, Canada; Microsat Systems Canada Inc., Canada; University of North Dakota, USA},
abstract = {Implementation, experimental evaluation and stability analysis of an admittance-controlled teleoperated pneumatic system is presented. A master manipulator navigates a pneumatic slave actuation, which interacts with a human arm as an environment. Considering the external force in the position control loop in the admittance control, enables the slave to handle the external force independent of the master. The proposed control system is evaluated experimentally using the admittance models with different settings. Stability of the control system is analyzed using the concept of Lyapunov exponents. Parametric stability analysis is conducted to show the effect of changing system parameters on stability. },
keywords = {admittance control; sliding mode control; pneumatic actuator; stability analysis; Lyapunov exponents},
language = {English}
}

Implementation, experimental evaluation and stability analysis of an admittance-controlled teleoperated pneumatic system is presented. A master manipulator navigates a pneumatic slave actuation, which interacts with a human arm as an environment. Considering the external force in the position control loop in the admittance control, enables the slave to handle the external force independent of the master. The proposed control system is evaluated experimentally using the admittance models with different settings. Stability of the control system is analyzed using the concept of Lyapunov exponents. Parametric stability analysis is conducted to show the effect of changing system parameters on stability.

@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{sadeghzadeh_2020,
title = {Affine Linear Parameter-Varying Embedding of Nonlinear Models with Improved Accuracy and Minimal Overbounding},
author = {Sadeghzadeh, A.; Sharif, B.; Toth, R.},
journal = {arXiv},
year = {2020},
institution = {Eindhoven University of Technology, Netherlands; Institute for Computer Science and Control, Hungary},
abstract = {In this paper, automated generation of linear parameter-varying (LPV) state-space models to embed the dynamical behavior of nonlinear systems is considered, focusing on the trade-off between scheduling complexity and model accuracy and on the minimization of the conservativeness of the resulting embedding. The LPV state-space model is synthesized with affine scheduling dependency, while the scheduling variables themselves are nonlinear functions of the state and input variables of the original system. The method allows to generate complete or approximative embedding of the nonlinear system model and also it can be used to minimize complexity of existing LPV embeddings. The capabilities of the method are demonstrated on simulation examples and also in an empirical case study where the first-principle motion model of a 3-DOF control moment gyroscope is converted by the proposed method to LPV model with low scheduling complexity. Using the resulting model, a gain-scheduled controller is designed and applied on the gyroscope, demonstrating the efficiency of the developed approach. },
language = {English}
}

In this paper, automated generation of linear parameter-varying (LPV) state-space models to embed the dynamical behavior of nonlinear systems is considered, focusing on the trade-off between scheduling complexity and model accuracy and on the minimization of the conservativeness of the resulting embedding. The LPV state-space model is synthesized with affine scheduling dependency, while the scheduling variables themselves are nonlinear functions of the state and input variables of the original system. The method allows to generate complete or approximative embedding of the nonlinear system model and also it can be used to minimize complexity of existing LPV embeddings. The capabilities of the method are demonstrated on simulation examples and also in an empirical case study where the first-principle motion model of a 3-DOF control moment gyroscope is converted by the proposed method to LPV model with low scheduling complexity. Using the resulting model, a gain-scheduled controller is designed and applied on the gyroscope, demonstrating the efficiency of the developed approach.

@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{ghaffari_2020,
title = {Analytical Design and Experimental Verification of Geofencing Control for Aerial Applications},
author = {Ghaffari, A.},
journal = {IEEE/ASME Transactions on Mechatronics},
year = {2020},
institution = {Wayne State University, USA},
abstract = {Keep-in operational envelopes are essential to maintain the safety of unmanned aerial vehicles (UAVs). System properties and constraints, including underactuated dynamics and actuator saturation, dramatically affect the system's maneuverability inside the operational envelope. Moreover, sate-of-the-art safety control depends heavily on the specifications of the operational envelope. Thus, this work focuses on creating a scalable technique to transform safety envelopes into input-constrained barriers along each axis of motion. Then, it is shown that the proposed class of operational envelopes simultaneously guarantees safety and asymptotic stability. The closed-form solution for the safety rule is derived as allowable low and high bounds of the control command, which are calculated in real-time. Furthermore, it is shown that the proposed safety design seamlessly integrates with an existing motion control algorithm with minimum modification. The super-twisting control (STC) is used to handle the nonlinear complexity of the UAV and parametric uncertainties and achieve a desirable robust behavior for trajectory and attitude control. The control calibration and tuning are carried out on a state-of-the-art experimental system. The experimental results verify the effectiveness of the proposed safety control. },
issn = {1941-014X },
keywords = {Safety, Attitude control, Trajectory, IEEE transactions, Mechatronics, Brushless DC motors, Scalability},
language = {English},
publisher = {IEEE}
}

Keep-in operational envelopes are essential to maintain the safety of unmanned aerial vehicles (UAVs). System properties and constraints, including underactuated dynamics and actuator saturation, dramatically affect the system's maneuverability inside the operational envelope. Moreover, sate-of-the-art safety control depends heavily on the specifications of the operational envelope. Thus, this work focuses on creating a scalable technique to transform safety envelopes into input-constrained barriers along each axis of motion. Then, it is shown that the proposed class of operational envelopes simultaneously guarantees safety and asymptotic stability. The closed-form solution for the safety rule is derived as allowable low and high bounds of the control command, which are calculated in real-time. Furthermore, it is shown that the proposed safety design seamlessly integrates with an existing motion control algorithm with minimum modification. The super-twisting control (STC) is used to handle the nonlinear complexity of the UAV and parametric uncertainties and achieve a desirable robust behavior for trajectory and attitude control. The control calibration and tuning are carried out on a state-of-the-art experimental system. The experimental results verify the effectiveness of the proposed safety control.

@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{cakmak-bolat_2020,
title = {Bending vibration control of a MR fluid embedded smart beam exposed by the conjunction of wind-induced galloping effects},
author = {Cakmak Bolat, F.; Sivrioglu, S.},
journal = {Smart Materials and Structures},
year = {2020},
number = {-1},
institution = {Abant Izzet Baysal University, Turkey; Gebze Technical University, Turkey},
abstract = {This study examined the control performance of a multi-layer smart beam structure in which the middle layer was partially filled with magnetorheological (MR) fluid. A galloping profile was connected to the endpoint to obtain vibration from this smart beam element in a regular regime. Flow-induced continuous vibration was created on the beam element by giving wind load from a certain distance to this galloping profile. The natural frequency values of the smart beam element were obtained analytically using the lumped parameter mathematical model, and their accuracy was compared with numerical methods. An electromagnet was placed opposite to MR fluid region to suppress vibrations by creating an actuator relationship with the smart beam. A norm-based H∞ robust control design was realized by taking the natural frequencies of the beam element into account. The control design was applied to the experimental system and the effectiveness of the controller was tested under various conditions. It was observed that the proposed MR fluid embedded active control structure has good properties to suppress the wind-induced vibrations in practice. },
keywords = {Smart-beam, active control, flow-induced excitation, galloping, smart materials},
language = {English},
publisher = {IOP Publishing}
}

This study examined the control performance of a multi-layer smart beam structure in which the middle layer was partially filled with magnetorheological (MR) fluid. A galloping profile was connected to the endpoint to obtain vibration from this smart beam element in a regular regime. Flow-induced continuous vibration was created on the beam element by giving wind load from a certain distance to this galloping profile. The natural frequency values of the smart beam element were obtained analytically using the lumped parameter mathematical model, and their accuracy was compared with numerical methods. An electromagnet was placed opposite to MR fluid region to suppress vibrations by creating an actuator relationship with the smart beam. A norm-based H∞ robust control design was realized by taking the natural frequencies of the beam element into account. The control design was applied to the experimental system and the effectiveness of the controller was tested under various conditions. It was observed that the proposed MR fluid embedded active control structure has good properties to suppress the wind-induced vibrations in practice.

This paper focuses on a two-dimensional flexible two-link manipulator that moves in a plane. The two links are simplified into Euler-Bernoulli beams respectively, and the coupled PDE-ODE dynamic model is established via Hamiltonian's principle. Two torque controllers that act on two motors respectively are designed for the simultaneous angular position control of two motors and suppression of the elastic vibrations of the links. Lyapunov direct method and extended LaSalle invariance principle are used to prove the asymptotic stability of the system. In addition, numerical simulations illustrate the correctness and reasonability of the theoretical proof for the stability analysis and boundary control design, and the experimental verification on the platform of Quanser laboratory also reaches the same conclusion.

@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.

@article{de-maity2_2020,
title = {Comparative Performance Study of Optimal Interval Type-2 Fuzzy PID Controllers with Practical System},
author = {Rani De (Maity), R.; Mudi, R.K.; Dey, C.},
journal = {International Journal of Computer Sciences and Engineering},
year = {2020},
month = {03},
volume = {8},
number = {3},
institution = {Jadavpur University, India; University of Calcutta, India},
abstract = {In this paper, the input and output scaling factors of the type-2 fuzzy PID Controller (IT2-FPID) are determined using three different optimization algorithms (Cuckoo search (CS), Particle swarm optimization (PSO), and Bee colony algorithm (BCA)) for a first-order integrating plus dead time (FOIPD) model. A comparative performance study is made for these three optimization algorithms in terms of various transient performance indices. The comparative analysis on the experimental results reveals that BCA based optimal IT2-FPID shows better performance on a simulation model whereas CS based optimal IT2-FPID is found to be superior for practical system over other algorithms. },
keywords = {Particle swarm optimization(PSO), Cuckoo search algorithm (CS), Bee colony algorithm(BCA), Interval type-2 fuzzy controller},
language = {English}
}

In this paper, the input and output scaling factors of the type-2 fuzzy PID Controller (IT2-FPID) are determined using three different optimization algorithms (Cuckoo search (CS), Particle swarm optimization (PSO), and Bee colony algorithm (BCA)) for a first-order integrating plus dead time (FOIPD) model. A comparative performance study is made for these three optimization algorithms in terms of various transient performance indices. The comparative analysis on the experimental results reveals that BCA based optimal IT2-FPID shows better performance on a simulation model whereas CS based optimal IT2-FPID is found to be superior for practical system over other algorithms.