Intelligent Control System Design for Nonlinear Systems Using an Improved TSK Wavelet Type-2 Fuzzy Brain Emotional Controller

This paper aims to propose a more efficient control algorithm for improving the control performance of uncertain nonlinear systems. A novel improved Takagi-Sugeno-Kang (TSK) wavelet type-2 fuzzy brain emotional learning controller (ITSKWT2FBC) is proposed. The proposed ITSKWT2FBC contains a brain emotional learning controller (BELC) combined with an improved TSK wavelet type-2 fuzzy system. By taking the advantages of BELC and the improved wavelet type-2 TSK fuzzy system, the control performance of this controller can be improved. The adaptive laws of controller parameters are derived from the defined Lyapunov function and gradient descent method. Moreover, the system stability can be guaranteed based on the Lyapunov stability theory. Finally, three nonlinear systems, a double inverted pendulum system, a Mackey-Glass time series prediction (TSP) and a four dimensional (4D) chaotic system synchronization are simulated to illustrate the effectiveness of the proposed method. The simulation results have demonstrated the effectiveness of the developed method.