Design of Model Predictive Control Weighting Factors for PMSM Using Gaussian Distribution-Based Particle Swarm Optimization
Fengxiang Wang, Jiaxiang Li, Zheng Li, Dongliang Ke, Jianming Du, Cristian García, José Rodríguez
Abstract
An improved particle swarm optimization (PSO) algorithm based on Gaussian distribution model is proposed to realize the autotuning of weighting factors for the cost function design in the model predictive control method. First, the design principle of the weighting factors in model predictive torque control for permanent magnet synchronous motor system is analyzed. Then, using the root mean square of the current error in the two-phase rotating coordinate system and the system switching frequency as references, the objective function of the particles in the PSO is designed by considering the main control goals of reducing the torque ripple, the current total harmonic distortion, and the switching frequency. The Gaussian individual optimal distribution model is used to update the particle position on the structure of the conventional PSO algorithm. The experimental results show that the proposed method can solve the problem of weighting factors design as it reduces the switching frequency of the system while achieving excellent steady-state performance.