Observer-based decentralized fuzzy neural sliding mode control for interconnected unknown chaotic systems via network structure adaptation

Abstract

In this paper, an observer-based fuzzy neural sliding mode control (OFNSMC) scheme for interconnected unknown chaotic systems is developed. The OFNSMC system is composed of a computation controller and a robust controller. The computation controller containing a self-structuring fuzzy neural network (SFNN) identifier is the principle controller, and the robust controller is designed to achieve L 2 tracking performance. The SFNN identifier uses the structure and parameter learning phases to perform the estimation of the interconnected unknown chaotic system dynamics. The structure learning phase consists of the growing of membership functions, the splitting of fuzzy rules and the pruning of fuzzy rules, and thus the SFNN identifier can avoid the time-consuming trial-and-error tuning procedure for determining the network structure of fuzzy neural network. The total states of the interconnected chaotic systems are not assumed to be available for measurement. Also, the unknown nonlinearities of the interconnected chaotic systems are not restricted to the systems output only. To demonstrate the effectiveness of the proposed method, simulation results are illustrated in this paper.