Weight Coefficient Setting of Current Predictive Control for Permanent Magnet Synchronous Machine using HPSO

Abstract

Aiming at the problem of multi-objective weight coefficient setting of model predictive control (MPC) for permanent magnet synchronous motor (PMSM), a hybrid particle swarm optimization (HPSO) algorithm with low computational complexity of fitness value is proposed to realize the self-setting of weight coefficient of cost function. In the proposed strategy, good particles update velocity and position through particle swarm optimization (PSO) algorithm, while bad particles not only do the same but generate the offspring by cross and mutation, and then the worse offspring will be replaced by their extremum individuals. It is faster that the adaptive cross and mutation rate makes the offspring get closer to the good particles, and it increases the diversity of particles without destroying the good particles. Experimental results show that compared with other optimization algorithms, the proposed algorithm. Firstly, is more inclined to escape from the local optimum. Secondly, it has higher search accuracy and faster convergence speed. Moreover, with setting weight coefficient, the system speed regulation time is shortened, the current total harmonic distortion (THD) is reduced significantly, and the switching frequency is effectively reduced without affecting the output power quality.