Journal Article•
On the Voltage-Based Control of Robot Manipulators
TL;DR: In this article, a novel approach for controlling electrically driven robot manipulators based on voltage control is presented, where feedback linearization is applied on the electrical equations of the dc motors to cancel the current terms which transfer all manipulator dynamics to the electrical circuit of motor.
Abstract: This paper presents a novel approach for controlling electrically driven robot manipulators based on voltage control. The voltage-based control is preferred comparing to torque-based control. This approach is robust in the presence of manipulator uncertainties since it is free of the manipulator model. The control law is very simple, fast response, efficient, robust, and can be used for high-speed tracking purposes. The feedback linearization is applied on the electrical equations of the dc motors to cancel the current terms which transfer all manipulator dynamics to the electrical circuit of motor. The control system is simulated for position control of the PUMA 560 robot driven by permanent magnet dc motors.
Citations
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10 Oct 2015
TL;DR: Simulation and experimental results both show that the proposed Voltage-based robust impedance control is superior to voltage-based impedance control in presence of uncertainties.
Abstract: Original Research Paper Received 31 May 2015 Accepted 19 June 2015 Available Online 13 July 2015 In this study, novel robust impedance control for lower-limb rehabilitation robotic system using voltage control strategy is used. Most existing control approaches are based on control torque strategy, which requires knowledge of robot dynamics as well as dynamics of patients. This obliges the controller to overcome complex problems such as uncertainty and nonlinearity involved in the dynamics of the system, robot and patients. Conversely, the voltage-based control approaches are free from the system dynamics. In addition, it considers the actuator dynamics. The performance of voltage-based approaches is demonstrated by experimental result in robotic applications. Compared with torque control scheme, it is simpler, less computational and more efficient. Nevertheless, uncertainty of actuator dynamics results in challenges for the voltage control strategy applications. The present paper presents novel robust impedance control based on the voltage control strategy. To overcome uncertainties, the adaptive fuzzy estimator is designed based on the voltage-based strategy. The proposed control is verified by stability analysis. To illustrate the effectiveness of the control approach, 1-DOF lower-limb rehabilitation robot is designed. Both torque-based impedance control and the voltage-based impedance control are compared through therapeutic exercise. It is shown that the voltage-based impedance control performs better than the traditional torque-based impedance control. Simulation and experimental results both show that the proposed voltage-based robust impedance control is superior to voltage-based impedance control in presence of uncertainties.
6 citations
14 Jan 2020
TL;DR: It was concluded that the H-Infinity controller achieved better performance and stability robustness characteristics for the joint torque control than the PID.
Abstract: This research work emphasizes on design of a robust control for a 3DOF robotic manipulator under uncertainties. The plant model was achieved using the independent joint method and the uncertainty problem was addressed by designing a robust controller using H-Infinity synthesis which was compared with PID. This was achieved with algorithms implemented in MATLAB. The H-Infinity controller recorded 0dB, while PID controller recorded 0.117dB and 0.061dB for joints I and II respectively in Complementary Sensitivity (T) graph at low frequencies. H-Infinity controller achieved better disturbance rejection characteristics with sensitivity (S) graph recording peak sensitivity of 0.817dB and 1.79dB at joints I and II respectively than PID controller which achieved 3dB and 1.86dB at joints I and II respectively. H-Infinity controller achieved better noise rejection characteristics with T graph recording lower gains at joints I and II respectively at high frequencies than PID controller which recorded higher gains at joints I and II respectively. Thus, it was concluded that the H-Infinity controller achieved better performance and stability robustness characteristics for the joint torque control than the PID. Original Research Article Agbaraji et al.; JERR, 9(4): 1-13, 2019; Article no.JERR.53565 2
6 citations
Cites background or methods from "On the Voltage-Based Control of Rob..."
...The electrical circuit of the actuator provides the equation [12,29]:...
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...3 many research works but recently, it has been criticized in Fateh [12], due to its limitations in feedback application and drawback in its application to the actuator inputs....
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...JERR.53565 many research works but recently, it has been criticized in Fateh [12], due to its limitations in feedback application and drawback in its application to the actuator inputs....
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...In [12] the manipulator was modeled based on independent joint method which is based on the joint actuator dynamic model and the torque due to link....
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01 Oct 2018
TL;DR: A Model-Reference Adaptive Control System (MRACS) is designed via the strategy of voltage control to provide an impedance control which is robust against structured and unstructured uncertainty and is simpler, more reliable and robust.
Abstract: In response to an environment, the robotic system’s desired dynamical behavior is modelled as a desired impedance, namely a reference model. The reference model, which is considered as an adaptive system gives the desired response to the contact force. Furthermore, a Model-Reference Adaptive Control System (MRACS) is designed via the strategy of voltage control to provide an impedance control which is robust against structured and unstructured uncertainty. Compared to the conventional adaptive impedance controls, which are designed by the strategy of torque control, it is simpler, more reliable and robust. The proposed control algorithm is simulated on an electrically-driven SCARA robotic system. Simulation results and comparison with an adaptive impedance control and force disturbance rejection-based adaptive controller are presented to show the control effectiveness.
5 citations
Cites background from "On the Voltage-Based Control of Rob..."
...In the past decade, the strategy of voltage control has been designed in robotics' control [26-27]....
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TL;DR: This paper addresses how to overcome the approximation error of the fuzzy system and uncertainties for asymptotic tracking control of robotic manipulators.
Abstract: Purpose – The purpose of this paper is to design a discrete indirect adaptive fuzzy controller for a robotic manipulator. This paper addresses how to overcome the approximation error of the fuzzy system and uncertainties for asymptotic tracking control of robotic manipulators. The uncertainties include parametric uncertainty, un-modeled dynamics, discretization error and external disturbances. Design/methodology/approach – The proposed controller is model-free and voltage-based in the form of discrete-time Mamdani fuzzy controller. The parameters of fuzzy controller are adaptively tuned for asymptotic tracking of a desired trajectory. A robust control term is used to compensate the approximation error of the fuzzy system. An adaptive mechanism is derived based on the stability analysis. Findings – The proposed model-free discrete control is robust against all uncertainties associated with the robot manipulator and actuators. The approximation error of the fuzzy system is well compensated to achieve asympt...
5 citations
TL;DR: A bi-level adaptive computed-current impedance controller for electrically driven robots is presented, utilizing the electrical equations of actuators, instead of the entire model of the electromechanical system.
Abstract: SUMMARY This paper presents a bi-level adaptive computed-current impedance controller for electrically driven robots. This study aims to reduce calculation complexities by utilizing the electrical equations of actuators, instead of the entire model of the electromechanical system. Moreover, taking the dynamical effects of mechanical parts into account through the current’s feedback, external disturbances are compensated. In order to handle uncertainties, a bi-level optimization problem is formulated to obtain guaranteed stability besides the estimation convergence. An adaptation rule and its optimal tuning gain are achieved. The proposed method is applied to control of a rehabilitation robot to evaluate its performance.
4 citations
Additional excerpts
...[54], where for a system like: { ẋ = f (x, u) y = h(x, u) (20)...
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References
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Book•
01 Jan 1986
TL;DR: This chapter discusses Jacobians: Velocities and Static Forces, Robot Programming Languages and Systems, and Manipulator Dynamics, which focuses on the role of Jacobians in the control of Manipulators.
Abstract: 1. Introduction. 2. Spatial Descriptions and Transformations. 3. Manipulator Kinematics. 4. Inverse Manipulator Kinematics. 5. Jacobians: Velocities and Static Forces. 6. Manipulator Dynamics. 7. Trajectory Generation. 8. Manipulator Mechanism Design. 9. Linear Control of Manipulators. 10. Nonlinear Control of Manipulators. 11. Force Control of Manipulators. 12. Robot Programming Languages and Systems. 13. Off-Line Programming Systems.
5,992 citations
"On the Voltage-Based Control of Rob..." refers background in this paper
...Many industrial robots use a form of so called PID control law [ 21 ] as...
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Book•
01 Jan 1989
TL;DR: This self-contained introduction to practical robot kinematics and dynamics includes a comprehensive treatment of robot control, providing background material on terminology and linear transformations and examples illustrating all aspects of the theory and problems.
Abstract: From the Publisher:
This self-contained introduction to practical robot kinematics and dynamics includes a comprehensive treatment of robot control. Provides background material on terminology and linear transformations, followed by coverage of kinematics and inverse kinematics, dynamics, manipulator control, robust control, force control, use of feedback in nonlinear systems, and adaptive control. Each topic is supported by examples of specific applications. Derivations and proofs are included in many cases. Includes many worked examples, examples illustrating all aspects of the theory, and problems.
3,736 citations
"On the Voltage-Based Control of Rob..." refers background in this paper
...The electrical circuit of the permanent magnet dc motor provides the following equation [ 14 ]...
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...So far, most industrial robots are controlled by independent joint control strategy while robots are high nonlinear multi-input/multi-output systems with complex couplings [ 14 ]....
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Book•
01 May 1991
TL;DR: Invention to Robotics provides both an introductory text for students coming new to the field and a survey of the state of the art for professional practitioners.
Abstract: From the Publisher:
Introduction to Robotics provides both an introductory text for students coming new to the field and a survey of the state of the art for professional practitioners.
2,354 citations
"On the Voltage-Based Control of Rob..." refers background in this paper
...Many industrial robots use a form of so called PID control law [21] as...
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TL;DR: The Robotics Toolbox is a software package that allows a MATLAB user to readily create and manipulate datatypes fundamental to robotics such as homogeneous transformations, quaternions and trajectories.
Abstract: The Robotics Toolbox is a software package that allows a MATLAB user to readily create and manipulate datatypes fundamental to robotics such as homogeneous transformations, quaternions and trajectories. Functions provided, for arbitrary serial-link manipulators, include forward and inverse kinematics, Jacobians, and forward and inverse dynamics. This article introduces the Toolbox in tutorial form, with examples chosen to demonstrate a range of capabilities. The complete Toolbox and documentation is freely available via anonymous ftp.
867 citations
"On the Voltage-Based Control of Rob..." refers methods in this paper
...The simulation model of PUMA 560 [24] is used in the control system....
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Book•
07 Apr 1988TL;DR: Model-based control of a robot manipulator has been studied in this paper, where the authors present the first integrated treatment of many of the most important recent developments in using detailed dynamic models of robots to improve their control.
Abstract: Model-Based Control of a Robot Manipulator presents the first integrated treatment of many of the most important recent developments in using detailed dynamic models of robots to improve their control. The authors' work on automatic identification of kinematic and dynamic parameters, feedforward position control, stability in force control, and trajectory learning has significant implications for improving performance in future robot systems. All of the main ideas discussed in this book have been validated by experiments on a direct-drive robot arm.The book addresses the issues of building accurate robot models and of applying them for high performance control. It first describes how three sets of models - the kinematic model of the links and the inertial models of the links and of rigid-body loads - can be obtained automatically using experimental data. These models are then incorporated into position control, single trajectory learning, and force control. The MIT Serial Link Direct Drive Arm, on which these models were developed and applied to control, is one of the few manipulators currently suitable for testing such concepts.Contents: Introduction. Direct Drive Arms. Kinematic Calibration. Estimation of Load Inertial Parameters. Estimation of Link Inertial Parameters. Feedforward and Computed Torque Control. Model-Based Robot Learning. Dynamic Stability Issues in Force Control. Kinematic Stability Issues in Force Control. Conclusion.Chae An is Research Staff Member, IBM T.J. Watson Research Center, Christopher Atkeson is an Assistant Professor and John Hollerbach is an Associate Professor in the MIT Department of Brain and Cognitive Sciences and the MIT Artificial Intelligence Laboratory. Model-Based Control of a Robot Manipulator is included in the Artificial Intelligence Series edited by Patrick Winston and Michael Brady.
452 citations