基于PSO的模型预测速度控制权重系数自整定

微特电机 ›› 2022, Vol. 50 ›› Issue (5): 42-46.

基于PSO的模型预测速度控制权重系数自整定

梅容魁¹, 于新红²

1.福州大学电气工程与自动化学院,福州 350108;
2.电机驱动与功率电子国家地方联合工程研究中心,中国科学院海西研究院泉州装备制造研究中心,泉州 362200

收稿日期:2022-03-14 出版日期:2022-05-28 发布日期:2022-06-22
作者简介:梅容魁(1996—),男,硕士研究生,研究方向为电机控制。于新红(1989—),男,硕士,工程师,研究方向为电力电子与电力传动。
基金资助:
福建省科技计划项目(2021I0039);福建省科技计划项目(2021T3064);福建省科技计划项目(2021T3035)

Self-tuning of Weight Coefficients for Model Predictive Speed Control Based on PSO

MEI Rongkui¹, YU Xinhong²

1. College of Electrical Engineering and Automation,Fuzhou University,Fuzhou 350108, China;
2. National and Local Joint Engineering Research Center for Electrical Drives and Power Electronics, Quanzhou Institute of Equipment Manufacturing Haixi Institutes Chinese Academy of Sciences,Quanzhou 362200, China

Received:2022-03-14 Online:2022-05-28 Published:2022-06-22

摘要/Abstract

摘要： 针对永磁同步电机(PMSM)模型预测速度控制,提出了一种基于改进粒子群优化(PSO)算法的权重系数在线自整定方法。由于传统模型预测速度控制包含多个权重系数,无法保证权重系数的最优。设计了一种混沌PSO算法,将实际电流与参考电流误差的均方根作为PSO算法的目标函数,通过迭代寻优获得符合最小化目标函数的权重系数。改进的PSO算法加强前期局部搜索能力,在搜索后期促进粒子收敛到最优全局解。实验结果验证了改进的PSO算法可实现权重系数实时的自整定,设计的权重系数使系统具有良好的稳态性能。

关键词: 永磁同步电机, 模型预测速度控制, 粒子群优化算法, 权重系数

Abstract: Aiming at the model predictive speed control of PMSM, an online self-tuning method of weight coefficient based on improved particle swarm optimization (PSO) algorithm was proposed. Since the traditional model predictive speed control contains multiple weight coefficients, the optimal weight coefficient cannot be guaranteed. A chaotic PSO algorithm was designed, which takes the root mean square of the error between the actual current and the reference current as of the objective function of the PSO algorithm, and obtains the weight coefficient that conforms to the minimized objective function through iterative optimization. The improved PSO algorithm strengthens the local searchability in the early stage and promotes the particles to converge to the optimal global solution in the later stage of the search. The experimental results verify that the improved PSO algorithm can realize the real-time self-tuning of the weight coefficient, and the designed weight coefficient makes the system have good steady-state performance.

Key words: permanent magnet synchronous machine(PMSM), model predictive speed control(MPSC), particle swarm optimization (PSO) algorithm, weight coefficient

中图分类号:

TM351

梅容魁, 于新红. 基于PSO的模型预测速度控制权重系数自整定[J]. 微特电机, 2022, 50(5): 42-46.

MEI Rongkui, YU Xinhong. Self-tuning of Weight Coefficients for Model Predictive Speed Control Based on PSO[J]. Small & Special Electrical Machines, 2022, 50(5): 42-46.

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