Hybrid model predictive control of nonlinear system based on PSO

GAO Peng, LIU Haoran, HAO Xiaochen, GUO Feng, SHI Xin
(Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China)

Abstract: In order to solve the problems of control accuracy is not high and stability is difficult to guarantee in complex nonlinear system, a hybrid model predictive control algorithm based on swarm optimization was introduced to nonlinear system. Fuzzy clustering and least squares method were used to set up the hybrid model, and the control values of nonlinear control systems were computed with the particle swarm optimization algorithm with compress factor. Fuzzy predictive control algorithm and hybrid model predictive control algorithm were separately evaluated on the nonlinear control systems. The Matlab simulations to the two algorithms were carried on to compare control accuracy and stability. The experimental results show that the hybrid model predictive control algorithm not only has high control accuracy, but also has robust.
Key words: nonlinear system; hybrid model predictive control; ANFIS; least squares; particle swarm optimization