Design of Self-Organizing Intelligent Controller Using Fuzzy Neural Network

Abstract

In this paper, a self-organizing intelligent controller (SOIC) is proposed for a class of nonlinear systems. The basic idea of this study is to use a self-organizing fuzzy neural network to imitate control law directly, and then, appeal to obtain a compact structure of controller to further reduce the computational burden and enhance the control performance. First, an effective criterion, using the tracking performance and structure risk of controller, is developed to self-organize the control rules online for SOIC to improve the tracking performance. Second, the structure and parameters of SOIC are updated by an adaptive projection-type algorithm to reduce the heavy computational burden to speed up the control response. Third, the stability of SOIC is proved in the sense of Lyapunov and the guidelines for selecting the control parameters are given. Finally, the effectiveness of SOIC is illustrated with three nonlinear systems. It is shown that the proposed SOIC can achieve better control performance in comparison with some other control schemes.