Pmsm speed control based on particle swarm optimization and deep deterministic policy gradient under load disturbance

Chiao Sheng Wang, Chen Wei Conan Guo, Der Min Tsay, Jau Woei Perng*

*此作品的通信作者

研究成果: Article同行評審

15 引文 斯高帕斯(Scopus)

摘要

Proportional integral-based particle swarm optimization (PSO) and deep deterministic policy gradient (DDPG) algorithms are applied to a permanent-magnet synchronous motor to track speed control. The proposed methods, based on notebooks, can deal with time delay challenges, imprecise mathematical models, and unknown disturbance loads. First, a system identification method is used to obtain an approximate model of the motor. The load and speed estimation equations can be determined using the model. By adding the estimation equations, the PSO algorithm can determine the sub-optimized parameters of the proportional-integral controller using the predicted speed response; however, the computational time and consistency challenges of the PSO algorithm are extremely dependent on the number of particles and iterations. Hence, an online-learning method, DDPG, combined with the PSO algorithm is proposed to improve the speed control performance. Finally, the proposed methods are implemented on a real platform, and the experimental results are presented and discussed.

原文English
文章編號343
期刊Machines
9
發行號12
DOIs
出版狀態Published - 12月 2021

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