Observer-based hybrid adaptive fuzzy neural tracking control for a class of unknown chaotic systems

Abstract

In this paper, an observer-based hybrid adaptive fuzzy neural controller (HAFNC) for a class of unknown chaotic systems is developed. The observer-based output feedback control law and a hybrid adaptive law to tune online the weighting factors of the adaptive fuzzy neural controller are derived. The total states of the chaotic system are not assumed to be available for measurement. The hybrid adaptive law utilizes two types of errors in the adaptive system, the tracking error and the modeling error. Based on strictly- positive-real (SPR) Lyapunov theory, the stability of the closed-loop system can be verified. To demonstrate the effectiveness of the proposed method, simulation results are illustrated in this paper.