Journal of Power Electronics

The Korean Institute of Power Electronics (전력전자학회)
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RLS-based deadbeat predictive current control for dual three-phase segmented powered linear motors

  • Shijiong Zhou (Institute of Electrical Engineering, Chinese Academy of Sciences) ;
  • Yaohua Li (Institute of Electrical Engineering, Chinese Academy of Sciences) ;
  • Liming Shi (Institute of Electrical Engineering, Chinese Academy of Sciences) ;
  • Keyu Guo (China North Vehicle Research Institute) ;
  • Manyi Fan (Institute of Electrical Engineering, Chinese Academy of Sciences) ;
  • Jinhai Liu (Institute of Electrical Engineering, Chinese Academy of Sciences)
  • Received : 2023.02.24
  • Accepted : 2023.12.26
  • Published : 2024.06.20
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Abstract

Deadbeat predictive current control (DPCC) is an effective model-based motor control method. However, due to the unbalanced inductance and parameter variations of the segmented powered linear motor stator, the conventional model of linear motors is not accurate, which ultimately affect the performance of the control. This paper proposes a novel DPCC based on the recursive least squares (RLS) method to identify the parameters of the dual three-phase segmented powered linear motor (SP-LM) model. First, the influence of unbalanced inductance caused by the segmented motor stator and parameter variations of the conventional DPCC are analyzed. Second, a discrete RLS model of the dual three-phase SP-LM is established, which is a common model for both linear induction motors (LIMs) and linear synchronous motors (LSMs). Finally, the model parameters are identified by the RLS method and the deadbeat principle is used to predict the current. The proposed method effectively eliminates the influence of unbalanced inductance and the parameter variation, improves the current control performance and reduces the thrust fluctuation. Experiments based on hardware-in-the-loop verify the proposed method.

Keywords

Acknowledgement

This work is supported by the research foundation of Institute of Electrical Engineering Chinese Academy of Sciences, (2021E1393201).

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