An Improved Model-Free Current Predictive Control Method for SPMSM Drives

IEEE Access

Shuo Zhang¹  Xingzhong Guo¹  Xuerong Li¹  Yongshen Li²  Xing Cui³  Yang Wang³ 

[1] Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment, Ministry of Education, Anhui Polytechnic University, Anhui, China;National Engineering Laboratory for Electric Vehicles, School of Mechanical Engineering, Beijing Institute of Technology, Beijing, China;North Vehicle Research Institute, Beijing, China;

关键词: Model-free predictive control;    parameter robustness;    surface-mounted permanent magnet synchronous machine;   

DOI  :  10.1109/ACCESS.2021.3115782

来源: DOAJ

【 摘 要 】

Traditional model predictive current control (MPCC) method depends on motor model for predictive control, when the motor parameters change with the working conditions, the predictive performance of MPCC will be deteriorated. To improve the parameter robustness of MPCC, a model-free current predictive control method that combines ultra-local model and sliding mode observer is proposed. First, the prediction model of MPCC based on the mathematical model of surface-mounted permanent magnet synchronous motor (SPMSM) is replaced by the ultra-local model that does not use any motor parameters. Second, the sliding mode observer is adopted to observe the parameter of ultra-local model and compensate parameter disturbance. Finally, the stability of the sliding mode observer is proved by the Lyapunov stability criterion. The traditional MPCC method and the proposed model-free current predictive control method are comparatively analyzed, simulation and experimental results show that the proposed model-free current predictive control method can improve the parameter robustness of MPCC.

【 授权许可】

Unknown