基于粒子群算法的永磁同步电机模型预测控制权重系数设计

Weighting Factors Design of Model Predictive Control for Permanent Magnet Synchronous Machine Using Particle Swarm Optimization

针对模型预测控制算法(MPC)在处理多目标多约束条件时权重系数设计问题,该文提出一种基于混沌变异的动态重组多种群粒子群算法(CDMSPSO)实现权重系数自整定。通过分析模型预测转矩控制(MPTC)代价函数,以两相旋转坐标系下电流误差方均根为参考,将降低转矩脉动和减小电流总谐波畸变(THD)作为主要控制目标,设计粒子群算法中粒子的目标函数。采用CDMSPSO算法,将整个种群划分为多个小的子粒子群,并以一定重组周期将粒子进行随机重组,然后随机选择一个子粒子群,以其中任一粒子为基础迭代生成混沌序列,并将新的混沌序列替代选择的子粒子群,实现粒子的混沌变异。仿真和实验结果验证了该方法能较好地解决权重系数整定问题,且稳态性能优异。

In this paper,a dynamic recombined multi-population particle swarm optimization algorithm based on chaotic-mutation(CDMSPSO)is proposed to realize self-tuning of the weighting factors when model predictive control algorithm(MPC)is dealing with multi-objective and multiconstraint conditions.By analyzing the design principle of cost function in the model predictive torque control(MPTC),taking the root mean square of the current error in the two-phase rotating coordinate system as a reference,the objective function of particles in particle swarm optimization is designed with reducing the torque ripple and reducing the current total harmonic distortion(THD)as the main control objectives.The whole population was divided into several small sub-particle swarms by using CDMSPSO,and the particles were randomly recombined with a certain recombination period,then a random sub-particle swarm is selected and chaotic sequence is generated iteratively on the basis of any particle,and the selected sub-particle swarm is replaced by the new chaotic sequence to realize chaotic mutation of particles.Simulation and experimental results show that this method can solve the problem of weighting factors setting well and achieve excellent steady-state performance.