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An Improved Model-Free Current Predictive Control Method for SPMSM Drives
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DOI：10.1109/ACCESS.2021.3115782.png)
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Abstract
En 中文
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.
Keywords:
Mathematical models
Predictive models
Predictive control
Stators
Robustness
Voltage control
Control systems
Model-free predictive control
parameter robustness
surface-mounted permanent magnet synchronous machine
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W