Convergence and robustness of a discrete-time learning control scheme for constrained manipulators

The constrained motion control is one of the most common control tasks found in many industrial robot applications. The nonlinear and nonclassical nature of the dynamic model of constrained robots make designing a controller for accurate tracking of both motion and force a difficult problem. In this article, a discrete-time learning control problem for precise path tracking of motion and force for constrained robots is formulated and solved. The control system is able to reduce the tracking error iteratively in the presence of external disturbances and errors in initial condition as the robot repeats its action. Computer simulation result is presented to demonstrate the performance of the proposed learning controller. © 1994 John Wiley & Sons, Inc.

[1]  Suguru Arimoto,et al.  Bettering operation of Robots by learning , 1984, J. Field Robotics.

[2]  Suguru Arimoto,et al.  Selective learning with a forgetting factor for robotic motion control , 1991, Proceedings. 1991 IEEE International Conference on Robotics and Automation.

[3]  Masaki Togai,et al.  Analysis and design of an optimal learning control scheme for industrial robots: A discrete system approach , 1985, 1985 24th IEEE Conference on Decision and Control.

[4]  Suguru Arimoto,et al.  Learning control theory for robotic motion , 1990 .

[5]  Suguru Arimoto,et al.  Robustness Issues of Learning Control for Robotic Motions , 1990 .

[6]  F. Miyazaki,et al.  Applications of learning method for dynamic control of robot manipulators , 1985, 1985 24th IEEE Conference on Decision and Control.

[7]  Danwei Wang,et al.  Learning control of constrained robots , 1992, Proceedings of the 1992 IEEE International Symposium on Intelligent Control.

[8]  Luca Maria Gambardella,et al.  On the iterative learning control theory for robotic manipulators , 1988, IEEE J. Robotics Autom..

[9]  S. Arimoto Mathematical Theory of Learning with Applications to Robot Control , 1986 .

[10]  S Arimoto,et al.  Convergence, stability and robustness of learning control schemes for robot manipulators , 1986 .

[11]  Giuseppe Casalino,et al.  Hybrid learning control for constrained manipulators , 1991, Adv. Robotics.

[12]  John J. Craig,et al.  Adaptive control of manipulators through repeated trials , 1984 .

[13]  Suguru Arimoto,et al.  Learning control for robot tasks under geometric constraints , 1994, Proceedings of the 1994 IEEE International Conference on Robotics and Automation.

[14]  N. H. McClamroch,et al.  Feedback stabilization and tracking of constrained robots , 1988 .

[15]  Christopher G. Atkeson,et al.  Robot trajectory learning through practice , 1986, Proceedings. 1986 IEEE International Conference on Robotics and Automation.

[16]  Roberto Horowitz,et al.  A new adaptive learning rule , 1991 .

[17]  Suguru Arimoto,et al.  Passivity and Learning Control for Robot Tasks under Geometric Constraints , 1992 .

[18]  John Hauser,et al.  Learning control for a class of nonlinear systems , 1987, 26th IEEE Conference on Decision and Control.

[19]  Toshiharu Sugie,et al.  An iterative learning control law for dynamical systems , 1991, Autom..

[20]  N. H. McClamroch,et al.  Position/force control design for constrained mechanical systems: Lyapunov's direct method , 1989, Proceedings of the 28th IEEE Conference on Decision and Control,.

[21]  Fumio Miyazaki,et al.  Robust leaning control , 1989, Proceedings of the 28th IEEE Conference on Decision and Control,.

[22]  Danwei Wang,et al.  Robust learning control for constrained robots , 1992, [1992] Proceedings of the 31st IEEE Conference on Decision and Control.