93-104
Indirect Learning Control For Nonlinear Dynamical Systems*
Yeong Soon Ryu** and Richard W. Longman***
Abstract
In a previous paper, learning control algorithms were developed based on adaptive control ideas for linear lime invariant systems. The learning control methods were shown lo have certain advantages over their adaptive control counterparts, such as the ability lo produce zero Tracking error in time varying systems, and the ability to eliminate repetative disturbances. In recent years, certain adaptive control algorithms have been developed for multi-body dynamic systems such as robots, with global guaranteed convergence lo zero tracking error for the nonlinear system equation. In this paper we study the relationship between such adaptive control methods designed for this specific class or nonlinear systems, and the learning control problem for such systems, seeking lo converge lo zero tracking error in following a specific command repeatedly, starting from the same initial conditions each lime. The extensions of these methods from the adaptive control problem to the learning control problem is seen lo be trivial. The advantages and disadvantages of using learning control based on such adaptive control concepts for nonlinear syslems and he use or other currently available learning control algorithms are discussed.
*Research supported by NASA Grant NAG n649.
**Graduate Research Assistant Department of Mechanical Engineering, Columbia University, Seeley W. Mudd Building New York New York 10027-6699
***Professor o~ Mechanical Engineering Columbia university, Seeley w. Mudd Building, New York, New York
10027-6699. Fellow AIM Pellow MS.