Journal of Power Electronics

The Korean Institute of Power Electronics (전력전자학회)
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Sensorless control of fault‑tolerant permanent magnet vernier rim‑driven motor based on improved model reference adaptive system

  • Tianrui Zhao (College of Marine Electrical Engineering, Dalian Maritime University) ;
  • Jingwei Zhu (College of Marine Electrical Engineering, Dalian Maritime University) ;
  • Qing Liu (College of Marine Electrical Engineering, Dalian Maritime University) ;
  • Jun Wu (College of Marine Electrical Engineering, Dalian Maritime University) ;
  • Yaqian Cai (College of Marine Electrical Engineering, Dalian Maritime University)
  • Received : 2024.04.30
  • Accepted : 2024.09.14
  • Published : 2025.04.20
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Abstract

To enhance the accuracy of rotor position and speed estimation in the sensorless vector control system of a fault-tolerant permanent magnet vernier rim-driven motor (FTPMV-RDM), a fuzzy fast super-twisting algorithm based model reference adaptive system (FFSTA-MRAS) method is proposed. A full-order current observer is designed to improve the estimation accuracy in an adjustable model in a FFSTA-MRAS. It incorporates the adjustable model and a calibration link to form a closed-loop estimation by introducing a correction term. The proportional integral (PI) adaptive law in a conventional MRAS is substituted with a fast super-twisting algorithm. To overcome the difficulty of selecting the sliding mode gain parameters in the fast super-twisting algorithm, a fuzzy control algorithm is introduced to obtain a reasonable sliding mode gain in real time, which can solve the contradictory problem of accuracy and chattering. Finally, a hardware experimental platform utilizing a StarSim controller is developed and experimental results demonstrate that the proposed method has superiority in terms of accurate estimation and minimum chattering under both healthy and one phase open-circuit fault conditions.

Keywords

Acknowledgement

This work was supported in part by the National Natural Science Foundation of China under Grant 52377037, in part by the Fundamental Research Funds for the Central Universities of China under Grant 3132023522, and in part by the Stability Support Project of the Laboratory of Science and Technology on Integrated Logistics Support, National University of Defense Technology under Grant WDZC20235250309.

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