Title :
Learning control for a class of nonlinear systems
Author_Institution :
University of California, Berkeley, California
Abstract :
We present and analyze a learning control algorithm for a class of nonlinear systems with linear input action. Sufficient conditions for the convergence of the trajectories to the desired trajectory are given. These conditions suggest a way to design a learning operator given a model of the system and provide a measure of how close the model should be to the actual system to guarantee convergence.
Keywords :
Algorithm design and analysis; Control system analysis; Control systems; Convergence; Error correction; Humans; Linear systems; Nonlinear control systems; Nonlinear systems; Sufficient conditions;
Conference_Titel :
Decision and Control, 1987. 26th IEEE Conference on
Conference_Location :
Los Angeles, California, USA
DOI :
10.1109/CDC.1987.272514
