Application of a General Learning Algorithm to the Control of Robotic Manipulators

In this paper, we discuss the use of a general learning algo rithm for the dynamic control of robot manipulators. Unlike some other learning control schemes, learning is based solely on observations of the input-output relationship of the system being controlled and is independent of control objectives. Information learned previously can be applied to new control objectives as long as similar regions of the system state space are involved. The control scheme requires no a priori knowl edge of the robot dynamics and is easy to apply to a particu lar control problem or to modify to accommodate changes in the physical system. The control scheme is computationally efficient and well suited to fixed-point implementation. The learning controller is evaluated in a series of computer simu lations involving a two-axis-articulated robot arm during simulated repetitive and nonrepetitive movements. We inves tigate the effects of varying learning algorithm parameters as well as control system performance in the presence of obser vation noise and changing manipulator payloads. The learn ing control system presented promises to provide good dy namic performance in complex situations at a reasonable cost as measured in terms of both hardware and software devel opment.

[1]  A. A. Mullin,et al.  Principles of neurodynamics , 1962 .

[2]  Richard Paul Collins Paul,et al.  Modelling, trajectory calculation and servoing of a computer controlled arm , 1972 .

[3]  James S. Albus,et al.  Theoretical and Experimental Aspects of a Cerebellar Model , 1973 .

[4]  James S. Albus,et al.  New Approach to Manipulator Control: The Cerebellar Model Articulation Controller (CMAC)1 , 1975 .

[5]  J. Albus.  Mechanisms of planning and problem solving in the brain , 1979 .

[6]  Steven Dubowsky,et al.  The application of model-referenced adaptive control to robotic manipulators , 1979 .

[7]  John M. Hollerbach,et al.  A Recursive Lagrangian Formulation of Manipulator Dynamics , 1980 .

[8]  J. Y. S. Luh,et al.  Resolved-acceleration control of mechanical manipulators , 1980 .

[9]  J. Y. S. Luh,et al.  On-Line Computational Scheme for Mechanical Manipulators , 1980 .

[10]  John M. Hollerbach,et al.  A Recursive Lagrangian Formulation of Maniputator Dynamics and a Comparative Study of Dynamics Formulation Complexity , 1980, IEEE Transactions on Systems, Man, and Cybernetics.

[11]  Suguru Arimoto,et al.  An adaptive trajectory control of manipulators , 1981 .

[12]  Suguru Arimoto,et al.  An Adaptive Method for Trajectory Control of manipulators , 1981 .

[13]  David E. Orin,et al.  Efficient Dynamic Computer Simulation of Robotic Mechanisms , 1982 .

[14]  Hendrik Van Brussel,et al.  A self-learning automaton with variable resolution for high precision assembly by industrial robots , 1982 .

[15]  Trevor Mudge,et al.  On The Control of Mechanical Manipulators , 1982 .

[16]  R.M. Dunn,et al.  Brains, behavior, and robotics , 1983, Proceedings of the IEEE.

[17]  Ten-Huei Guo,et al.  Adaptive linear controller for robotic manipulators , 1983 .

[18]  Lorenzo Sciavicco,et al.  An Adaptive Model Following Control for Robotic Manipulators , 1983 .

[19]  C.s.g. Lee,et al.  An adaptive control strategy for mechanical manipulators , 1984 .

[20]  Chun-Hsien Chung,et al.  Adaptive self-tuning control of manipulators in task coordinate system , 1984, ICRA.

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

[22]  Jean-Jacques E. Slotine,et al.  The Robust Control of Robot Manipulators , 1985 .

[23]  N. Kirćanski,et al.  An efficient procedure for generating dynamic manipulator models , 1985, Robotica.

[24]  Marc Richetin,et al.  Inference of Automata by dialectic learning , 1985, Robotica.

[25]  Ashoke Kumar Datta,et al.  A fuzzy model for learning in automata , 1985, Robotica.