Title :
A Locally Weighted Learning Method for Online Approximation Based Control
Author :
Zhao, Y. ; Farrell, J.A.
Author_Institution :
Department of Electrical Engineering, University of California, Riverside
Abstract :
This article is concerned with tracking control problems for nonlinear systems that are not affine in the control signal and that contain unknown nonlinearities in the system dynamic equations. This paper develops a piecewise linear approximation to the unknown functions during the system operation. New control and parameter adaptation algorithms are designed and analyzed using Lyapunov-like methods. The objectives are to achieve global stability of the state, accurate tracking of bounded reference signals contained within a known domain D, and at least boundedness of the function approximation parameter estimates.
Keywords :
Adaptive approximation based control; adaptive nonlinear control; locally weighted learning; receptive field weighted regression; Algorithm design and analysis; Control nonlinearities; Control systems; Function approximation; Learning systems; Nonlinear control systems; Nonlinear dynamical systems; Nonlinear equations; Nonlinear systems; Piecewise linear approximation; Adaptive approximation based control; adaptive nonlinear control; locally weighted learning; receptive field weighted regression;
Conference_Titel :
Decision and Control, 2005 and 2005 European Control Conference. CDC-ECC '05. 44th IEEE Conference on
Print_ISBN :
0-7803-9567-0
DOI :
10.1109/CDC.2005.1582570
