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

doi:10.19595/j.cnki.1000-6753.tces.200752

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摘要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。

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关键词 ：永磁同步电机, 模型预测控制, 权重系数, 粒子群优化, 动态重组, 混沌变异

Abstract：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 multi-constraint 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.

Key words： Permanent magnet synchronous motor model predictive control weighting factors particle swarm optimization dynamic recombination chaotic mutation

收稿日期: 2020-06-30

PACS:

TM341

基金资助:国家自然科学基金项目（51877207）和中国科学院海西研究院“前瞻跨越” 计划重大项目（CXZX-2018-Q01）资助

通讯作者: 汪凤翔,男,1982年生,研究员,博士生导师,研究方向为电机驱动与电力电子。E-mail：fengxiang.wang@fjirsm.ac.cn

作者简介: 李家祥,男,1996年生,硕士研究生,研究方向为电机控制和智能优化算法。E-mail：jiaxiang_li163@163.com

引用本文:

李家祥, 汪凤翔, 柯栋梁, 李政, 何龙. 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[J]. 电工技术学报, 2021, 36(1): 50-59. Li Jiaxiang, Wang FengxiangKe Dongliang, Li Zheng, He Long. Weighting Factors Design of Model Predictive Control for Permanent Magnet Synchronous Machine Using Particle Swarm Optimization. Transactions of China Electrotechnical Society, 2021, 36(1): 50-59.

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