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.
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TL;DR: A new adaptive fuzzy voltage tracking control law is developed to ensure that all variables of the closed-loop system are semi-globally uniformly ultimately bounded.
Abstract: The aim of this paper is to tackle the problem of adaptive fuzzy voltage-based tracking control for uncertain electrically driven robotic manipulators subject to input delay and partial state constraints in a unified framework. With the aid of barrier Lyapunov function-based backstepping method and adaptive fuzzy approximators, the proposed method is constructed for uncertain robotic systems in the framework of voltage control strategy. This is intended to convert robot control problem to motor control problem. Based on input integral technique, a new variable is introduced for the system such that the input-delayed robotic system is turned to the non-delayed robotic system. Furthermore, the number of adaptive learning parameters is free from the number of subsystems. In other words, only one adaptive parameter is adjusted online for each joint to reduce computational burden; hence, a new adaptive fuzzy voltage tracking control law is developed to ensure that all variables of the closed-loop system are semi-globally uniformly ultimately bounded. The tracking error of joint positions also converges to a small neighborhood around the origin such that the constraints on the joint angular positions and velocities are not transgressed during operation. Various scenarios for numerical simulations are given to show the potential of the proposed control algorithm when applied to a robot manipulator driven by permanent magnet dc motors.
23 citations
TL;DR: The proposed decentralized Direct Adaptive Fuzzy Control (DAFC) of electrically driven robot manipulators using the voltage control strategy is simple, in a decentralized structure with high-accuracy response, robust tracking performance, and guaranteed stability.
Abstract: Decentralized control is the most favorite control of robot manipulators due to computational simplicity and ease of implementation. Beside that, adaptive fuzzy control efficiently controls uncertain nonlinear systems. These motivate us to design a decentralized fuzzy controller. However, there are some challenging problems to guarantee stability. The state-space model of the robotic system including the robot manipulator and motors is in a noncompanion form, multivariable, highly nonlinear, and heavily coupled with a variable input gain matrix. For this purpose, adaptive fuzzy control may use all variable states. As a result, it suffers from computational burden. To overcome the problems, we present a novel decentralized Direct Adaptive Fuzzy Control (DAFC) of electrically driven robot manipulators using the voltage control strategy. The proposed DAFC is simple, in a decentralized structure with high-accuracy response, robust tracking performance, and guaranteed stability. Instead of all state variables, only the tracking error of every joint and its derivative are given as the inputs of the controller. The proposed DAFC is simulated on a SCARA robot driven by permanent magnet dc motors. Simulation results verify superiority of the decentralized DAFC to a decentralized PD-fuzzy controller.
22 citations
TL;DR: A novel approach to neural network based tracking-control of robot manipulator including actuator dynamics is proposed by using of backstepping method where structured and unstructured uncertainties in robot dynamics and actuator model are approximated by this neural controller.
Abstract: A novel approach to neural network based tracking-control of robot manipulator including actuator dynamics is proposed by using of backstepping method. A simple two-step backstepping is considered for an n- link robotic system, and a feedforward neural controller is designed at second step where structured and unstructured uncertainties in robot dynamics and actuator model are approximated by this neural controller. Bounds of network reconstruction error and other imprecisions are estimated adaptively and for compensating them, a robust control signal is added and modified. Stability analysis is performed by the Lyapunov direct method and performance efficiency of the proposed controller is justified by the simulations.
20 citations
TL;DR: Simulation results on an articulated robot driven by permanent magnet DC motors, and experimental implementation show that the proposed model-free controller has a satisfactory performance as compared to an adaptive uncertainty estimation-based controller.
20 citations
TL;DR: The Lyapunov stability theorem shows that the controlled closed-loop system under the VB-ASMC has global asymptotic stability.
Abstract: This study investigates a voltage-based adaptive sliding mode control (VB-ASMC) to tracking the position of an $n$ rigid-link flexible-joint (RLFJ) robot manipulator under the presence of uncertainties and external disturbances. First, the dynamic equations of the $n$-RLFJ robot manipulator have been divided into $n$ subsystems, and for each of them a voltage-based sliding mode control (VB-SMC) is designed simultaneously. The mathematical proof shows that the closed-loop system under VB-SMC has global asymptotic stability. Second, due to the use of the sign function in the VB-SMC structure, the occurrence of chattering is inevitable. Therefore, to overcome this problem, an adaptive estimator is designed to estimate the boundary of uncertainties. Since the adaptive estimator part in the VB-ASMC has only one law, the proposed control has a very low computational volume. The Lyapunov stability theorem shows that the controlled closed-loop system under the VB-ASMC has global asymptotic stability. Finally, extensive simulations on the single and 2-RLFJ robot manipulator and practical implementation on the single-RLFJ robot manipulator are presented to demonstrate the effectiveness and improved performance of the proposed control scheme.
19 citations
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...
[...]
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 ]...
[...]
...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 ]....
[...]
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...
[...]
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....
[...]
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