摘要
针对永磁同步电机(PMSM)系统中模型参数失配和一拍延时导致电流响应精度下降的问题,提出了一种新型鲁棒无差拍预测电流控制(NR-DPCC)方法。首先,建立了PMSM的预测电流控制模型,详细分析了各电磁参数对常规DPCC的敏感性。其次,采用Adaline神经网络的方法设计了电感和磁链参数辨识器;在此基础上,提出了一种应用于电机系统的变步长神经网络权值调整算法;最后,基于在线辨识的参数值和电流预测值,提出了NR-DPCC方法,并通过RT-Lab硬件在环仿真实验平台进行了验证。研究结果表明,相较于常规DPCC方法,所提方法不仅能够精确的在线跟踪电机的参数变化,而且有效的提高了电流响应精度。
Aiming at the problem of decreasing current response accuracy caused by model parameter mismatch and one-beat delay in permanent magnet synchronous motor(PMSM)system,a novel robust deadbeat predictive current control(NR-DPCC)method is proposed.Firstly,the predictive current control model of PMSM is established,and the sensitivity of electromagnetic parameters to conventional DPCC is analyzed in detail.Secondly,the inductance and flux linkage parameter identifier is designed by using Adaline neural network.On this basis,a variable step size neural network weight adjustment algorithm is proposed for motor systems.Finally,based on the parameters and current prediction values of online identification,the NR-DPCC method is proposed and verified by the RT-Lab hardware in the loop simulation experiment platform.The research results show that compared with the conventional DPCC method,the proposed method can not only accurately track the parameter changes of the motor online,but also effectively improve the current response accuracy.
作者
粟慧龙
汤梦姣
程翔
SU Huilong;TANG Mengjiao;CHENG Xiang(School of Intelligent Control,Hunan Railway Professional Technology College,Zhuzhou 412001,China)
出处
《组合机床与自动化加工技术》
北大核心
2023年第9期92-96,101,共6页
Modular Machine Tool & Automatic Manufacturing Technique
基金
教育部高等学校科学研究发展中心专项课题项目(ZJXF2022060)
湖南省教育厅科学研究项目(22C1102,21C1295)
湖南省自然科学基金资助项目(2023JJ6004,2023JJ60232)。
关键词
永磁同步电机
预测电流控制
参数在线辨识
模型参数失配
permanent magnet synchronous motor
predictive current control
parameter online identification
model parameter mismatch
