Design of self-constructing fuzzy wavelet neural control system

In this paper, a self-constructing fuzzy wavelet neural network (SFWNN) is used to approximate an unknown nonlinear term in the system dynamics with the structure and parameter learning abilities concurrently. Further, a self-constructing fuzzy wavelet neural control (SFWNC) system, which is composed of a computation controller and a robust compensator, is proposed. The computation controller using the SFWNN approximator is the main controller and the robust compensator is designed to eliminate the effect of the approximation error. All controller parameters of the SFWNC system are adaptively update based on the Lyapunov stability theory and the projection algorithm to guarantee the closed-loop system stability. Finally, the effectiveness of the proposed SFWNC system is verified by simulation results and the SFWNN approximator has the admirable property of small fuzzy rules size and high learning accuracy.