A Locally Weighted Learning Method for Online Approximation Based Control

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.