Robust self-organizing fuzzy-neural control using asymmetric Gaussian membership functions

Abstract

A robust self-organizing fuzzy-neural control (RSOFNC) system is proposed in this paper. The RSOFNC system is comprised of a self-structuring fuzzy neural network (SFNN) controller and a robust controller. The SFNN controller is the principal controller and the robust controller is designed to achieve L2 tracking performance. In the SFNN controller design, a SFNN with the asymmetric Gaussian membership functions is used to online approximate an ideal controller via the structure and parameter learning phases. The structure learning phase consists of the growing of membership functions and the pruning of fuzzy rules, and thus the SFNN can avoid the time-consuming trial-and-error tuning procedure for determining the network structure of fuzzy neural network. Finally, the proposed RSOFNC system is applied to control a second-order chaotic system. The simulation results show that the proposed RSOFNC system can achieve favorable tracking performance.