基于超局部模型的永磁同步电机预测控制  

Predictive Control of Permanent Magnet Synchronous Motor Based on Ultralocal Model

作　　者：吴艳娟[1,2] 林峻山[1,2] 王云亮 WU Yanjuan;LIN Junshan;WANG Yunliang(School of Electric Engineering and Automation,Transmission and Intelligent Control,Tianjin University of Technology,Tianjin 300384,China;Tianjin Key Laboratory for Control Theory&Applications in Complicated Systems,Transmission and Intelligent Control,Tianjin University of Technology,Tianjin 300384,China;Tianjin Key Laboratory of New Energy Power Conversion,Transmission and Intelligent Control,Tianjin University of Technology,Tianjin 300384,China)

机构地区：[1]天津理工大学电气工程与自动化学院,天津300384 [2]天津理工大学天津市复杂系统控制理论及应用重点实验室,天津300384 [3]天津理工大学天津市新能源电力变换传输与智能控制重点实验室,天津300384

出　　处：《组合机床与自动化加工技术》2023年第11期51-55,共5页Modular Machine Tool & Automatic Manufacturing Technique

基　　金：天津市科技计划项目(18ZXYEN00100);中央引导地方科技发展资金项目(22ZYCGSN00190)。

摘　　要：针对工况变化时永磁同步电机参数随之变化引起的传统有限集模型预测电流控制性能下降的问题,提出了一种采用自适应径向基函数神经网络观测器的超局部模型预测控制方法。首先基于超局部模型,建立无电机参数的永磁同步电机模型;其次,设计自适应径向基函数神经网络观测器去逼近所建立的模型中的未知部分,通过李雅普诺夫稳定性定理进行了稳定性分析;最后,对两种方法进行实验比较,结果表明所提出的方法具有更好的动态和稳态性能。Aiming at the problem of the degradation of current control performance predicted by the traditional finite set model caused by the subsequent change of parameters of permanent magnet synchronous motor when working conditions change,a ultralocal model predictive control method using adaptive radial basis function neural network observer is proposed.Firstly,based on the ultralocal model,a permanent magnet synchronous motor model without motor parameters is established.Secondly,an adaptive radial basis function neural network observer is designed to approximate the unknown part of the established model,and the stability analysis is carried out by Lyapunov′s stability theorem.Finally,the experimental comparison of the two methods shows that the proposed method has better dynamic and steady-state performance.

关 键 词：永磁同步电机 超局部模型 预测控制 自适应径向基函数神经网络观测器 

分 类 号：TH165[机械工程—机械制造及自动化] TG659[金属学及工艺—金属切削加工及机床]