Terminal sliding mode control of nonlinear chaotic systems using self-recurrent wavelet neural network

In this paper, we design a self-recurrent wavelet neural network (SRWNN) based terminal sliding mode controller for nonlinear chaotic systems with uncertainties. The nonlinear chaotic systems are decomposed into a sum of a nominal nonlinear part and uncertainty term. The terminal sliding mode control (TSMC) method which has been used to control the system robustly can drive the tracking errors to zero in a finite time. In addition, the TSMC has the advantages such as improved performance, robustness, reliability and precision by contrast with the classical sliding mode control (CSMC). For the control of nonlinear chaotic system with various uncertainties, we employ the SRWNN which is used for the prediction of uncertainties. The weights of SRWNN are trained by adaptive laws based on Lyapunov stability theorem. Finally, we carry out simulations on two nonlinear chaotic systems such as Duffing system and Lorenz system to illustrate the effectiveness of the proposed control.