Adaptive Control of Nonlinear Systems Using Multiple Models with Second-Level Adaptation

Abstract

Adaptive control of a class of single-input single-output (SISO) nonlinear systems with large parametric uncertainties has been investigated in this paper. Control of nonlinear systems using adaptive schemes suffers from the drawback of poor transient responses in parametrically uncertain environment. The use of multiple models presents a solution to this problem. In this paper, state transformation and feedback linearization have been used to algebraically transform nonlinear system dynamics to linear ones. The unknown parameter vector for the plant is assumed to be bounded within a set of compact parameter space. Indirect adaptive control using multiple identification models has been used to improve transient response and convergence time. The observer-based identifier model is used for all these models. Lyapunov stability analysis is used to obtain tuning laws for estimator parameters. Further, second-level adaptation using combination of all the adaptive estimator models is used. Simulations have demonstrated that multiple models with second-level adaptation yield better transient performance with faster convergence.