粒子群优化
控制理论(社会学)
加权
转矩脉动
模型预测控制
高斯分布
扭矩
工程类
总谐波失真
计算机科学
数学优化
直接转矩控制
数学
算法
控制(管理)
感应电动机
人工智能
电压
物理
电气工程
热力学
量子力学
声学
作者
Fengxiang Wang,Jiaxiang Li,Zheng Li,Dongliang Ke,Jialu Du,Cristian Garcia,José Rodríguez
出处
期刊:IEEE Transactions on Industrial Electronics
[Institute of Electrical and Electronics Engineers]
日期:2022-11-01
卷期号:69 (11): 10935-10946
被引量:17
标识
DOI:10.1109/tie.2021.3120441
摘要
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.
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