Chaotic Newton-Raphson Optimization Based Predictive Control for Permanent Magnet Synchronous Motor Systems with Long-Delay

Abstract

A Tent-map chaotic Newton-Raphson optimization based neural network predictive control (TCNR-NPC) is developed to apply to the long-delay permanent magnet synchronous motor (PMSM) system in this paper. Due to a nonlinear model utilized in the predictive controller, nonlinear optimization methods turn into an important issue. To overcome the shortcoming of the conventional nonlinear programming on the initial condition sensitivity and maintain the accuracy of optimal solution, chaos optimization algorithm (COA) and Newton-Raphson (NR) are combined. With the comparison of COA and NR based optimization methods, our approach, the Tent-map chaotic Newton-Raphson (TCNR) optimization, is easier to reach the global optimum; thus, it would be employed in neural network predictive control. It is found that TCNR-NPC has a better performance than those of GPC, modified GPC, adaptive extended PSO based NPC, and PSO based PI controllers in real experiments.