tracking control for nonlinear multivariable systems using wavelet-type TSK fuzzy brain emotional learning with particle swarm optimization

This paper studies the H∞ tracking control for uncertain nonlinear multivariable systems. We propose a strategy, which combines adaptive wavelet-type Takagi-Sugeno-Kang (TSK) fuzzy brain emotional learning controller (WTFBELC) and robust compensator. As WTFBELC, it is main designed to mimic ideal controller. The proposed WTFBELC obtain much better ability of handling nonlinearities uncertainties, but compensator compensate residual error between Furthermore, optimal rates are searched quickly by using particle swarm optimization (PSO) algorithm, parameter updated laws derived based on steepest descent gradient method. performance this novel scheme guaranteed Lyapunov stability theory. mass-spring-damper mechanical system three-link robot manipulator, used verify effectiveness PSO-WTFBELC scheme.