Model Predictive Control of Permanent Magnet Synchronous Motor Based on State Transition Constraint Method

doi:10.1155/2021/3171417

	Model Predictive Control of Permanent Magnet Synchronous Motor Based on State Transition Constraint Method
	Gao, Jun1 ; Zhang, Jie 1; Fan, Mengyang1 ; Peng, Zhiyuan 2; Chen, Qinghong1 ; Zhang, Heshan 3
	2021
发表期刊	MATHEMATICAL PROBLEMS IN ENGINEERING
ISSN	1024-123X
EISSN	1563-5147
卷号	2021
摘要	Permanent magnet synchronous motors are widely used and have sufficient development prospects in the drive systems of electric vehicles. Traditional model predictive control (MPC) methods are shown to achieve good control performance by tracking the d-and q-axis current as well as limiting the current amplitude. However, the dynamic response performance and current harmonics during the switching process are not considered in the traditional MPC. Therefore, this paper proposes an MPC that can effectively improve control performance, where the switch transfer sequence in the switch constraint module is considered in the improved model. The state transition error is obtained from the switch constraint module according to the current switch state and the transition probability, after which, the integration into the cost function in which the driving error, tracking error, and constraint error are considered. A reinforcement learning (RL) algorithm is used to obtain the weight coefficient of the transition error term in the constraint module for automatically determining the best switch state for the next control period using the cost function. Simulation tests show that the total harmonic distortion of the phase current based on the improved MPC is 978.4%, less than 2843.0% of the traditional MPC method under 20 Nm at 1000 rpm. The torque response time of the motor is reduced by 0.026 s, whereas the simulation results indicate that the 100 km acceleration performance of an electric vehicle is improved by 9.9%. © 2021 Jun Gao et al.
关键词	Cost functions Electric machine control Electric vehicles Errors Permanent magnets Predictive control systems Reinforcement learning Synchronous motors Traction motors Constraint module Control performance Cost function Model predictive control Permanent Magnet Synchronous Motor Predictive control methods State transitions Switch state Traditional models Transition errors
DOI	10.1155/2021/3171417
收录类别	EI
语种	英语
出版者	Hindawi Limited
EI入藏号	20215011315279
EI分类号	704.1 Electric Components ; 705.3.1 AC Motors ; 723.4 Artificial Intelligence ; 731.1 Control Systems ; 731.2 Control System Applications ; 921.5 Optimization Techniques
原始文献类型	Journal article (JA)
引用统计	被引频次：3[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	https://ir.cqcet.edu.cn/handle/39TD4454/10379
专题	智能制造与汽车学院
作者单位	1.Chongqing College of Electronic Engineering, Chongqing; 401331, China; 2.Chongqing Chang'an New Energy Automobile Technology Co., Ltd, Chongqing; 401135, China; 3.Chongqing Jiaotong University, Chongqing; 400074, China
第一作者单位	重庆电子科技职业大学
推荐引用方式 GB/T 7714	Gao, Jun,Zhang, Jie,Fan, Mengyang,et al. Model Predictive Control of Permanent Magnet Synchronous Motor Based on State Transition Constraint Method[J]. MATHEMATICAL PROBLEMS IN ENGINEERING,2021,2021.
APA	Gao, Jun,Zhang, Jie,Fan, Mengyang,Peng, Zhiyuan,Chen, Qinghong,&Zhang, Heshan.(2021).Model Predictive Control of Permanent Magnet Synchronous Motor Based on State Transition Constraint Method.MATHEMATICAL PROBLEMS IN ENGINEERING,2021.
MLA	Gao, Jun,et al."Model Predictive Control of Permanent Magnet Synchronous Motor Based on State Transition Constraint Method".MATHEMATICAL PROBLEMS IN ENGINEERING 2021(2021).