Adaptive control of nonlinear systems using multiple identification models

Abstract

Adaptive control of nonlinear systems is considered in this study. Available methods in this field are reviewed first. Focusing on the minimum-phase, input-output linearizable and linearly parameterized nonlinear systems, direct and indirect adaptive controllers are developed for the cases of matched and unmatched uncertainties. A new methodology is proposed, which makes use of multiple identification models in order to improve the transient performance under large parametric uncertainties. Adaptation and switching mechanisms are developed based on the use of multiple Lyapunov functions and cost functions. The resulting closed loop systems are shown to be stable with the switching mechanisms. Combination of direct and indirect adaptive control schemes is also presented for a class of nonlinear systems which do not give rise to over-parametrization. The theoretical results obtained in this study are verified by computer simulations.