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Model Predictive Control Method Based on Data-Driven Approach for Permanent Magnet Synchronous Motor Control System

基于数据驱动的永磁同步电机控制系统模型预测控制方法

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Abstract

Permanent magnet synchronous motor (PMSM) is widely used in alternating current servo systems as it provides high efficiency, high power density, and a wide speed regulation range. The servo system is placing higher demands on its control performance. The model predictive control (MPC) algorithm is emerging as a potential high-performance motor control algorithm due to its capability of handling multiple-input and multiple-output variables and imposed constraints. For the MPC used in the PMSM control process, there is a nonlinear disturbance caused by the change of electromagnetic parameters or load disturbance that may lead to a mismatch between the nominal model and the controlled object, which causes the prediction error and thus affects the dynamic stability of the control system. This paper proposes a data-driven MPC strategy in which the historical data in an appropriate range are utilized to eliminate the impact of parameter mismatch and further improve the control performance. The stability of the proposed algorithm is proved as the simulation demonstrates the feasibility. Compared with the classical MPC strategy, the superiority of the algorithm has also been verified.

摘要

永磁同步电动机因其具有高效率、高功率密度和宽调速范围而广泛应用于交流伺服系统。伺服系统对控制性能提出了更高的要求。模型预测控制算法因具有处理多输入和多输出变量以及附加约束的能力，成为一种潜在的高性能电机控制算法。对于在永磁同步电机控制过程中应用模型预测控制算法，存在着由电机的电磁参数变化或负载扰动引起的非线性扰动，这将导致标称模型和受控对象之间存在失配问题，从而导致预测误差，影响控制系统的动态稳定性。本文提出了一种数据驱动的模型预测控制策略，利用适当范围内的历史数据以消除参数失配带来的影响，并进一步提高控制性能。仿真展示的可行性验证了该算法的稳定性，并与经典模型预测控制策略进行了比较，验证了算法的优越性。

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References

  1. WANG Q, YU H T, WANG M, et al. An improved sliding mode control using disturbance torque observer for permanent magnet synchronous motor [J]. IEEE Access, 2019, 7: 36691–36701.

    Article  Google Scholar 

  2. FANG J Q, DENG W X, YAO J Y, et al. A fast adaptive disturbance rejection control for motor servo systems [J]. Journal of Xi’an Jiaotong University, 2021, 55(6): 44–52 (in Chinese).

    Google Scholar 

  3. SUN Y J, ZHOU S G, ZHANG K C. Control of permanent magnet synchronous motors based on a novel double-vector model prediction [J]. Electrical Automation, 2021, 43(3): 36–38, 54 (in Chinese).

    Google Scholar 

  4. ZHANG C Z, ZHUANG C, ZHENG X K, et al. Stochastic model predictive control approach to autonomous vehicle lane keeping [J]. Journal of Shanghai Jiao Tong University (Science), 2021, 26(5): 626–633.

    Google Scholar 

  5. LI D W, XI Y G, ZHENG P Y. Constrained robust feedback model predictive control for uncertain systems with polytopic description [J]. International Journal of Control, 2009, 82(7): 1267–1274.

    Article  MathSciNet  MATH  Google Scholar 

  6. WANG N Q, LI D W, XI Y G. The trajectory planning of 9 degree-of-freedom manipulator based on model predictive control method [C]//2016 35th Chinese Control Conference. Chengdu: IEEE, 2016: 4247–4252.

  7. XIAO L F, MA L M, HUANG X H. Intelligent fractional-order integral sliding mode control for PMSM based on an improved cascade observer [J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(2): 328–338.

    Article  Google Scholar 

  8. YANG L Y, LU J B, XU Y W, et al. Constrained data-driven RMPC with guaranteed stability [C]//2019 12th Asian Control Conference. Kitakyushu: IEEE, 2019: 1277–1282.

    Google Scholar 

  9. LI J. Prediction error analysis and suppression of model predictive current control for PMSM drives [D]. Hangzhou: Zhejiang University, 2019 (in Chinese).

    Google Scholar 

  10. CHEN X, ZHAO W X, JI J H, et al. Model predictive current control of double-side linear vernier permanent magnet machines considering end effect [J]. Transactions of China Electrotechnical Society, 2019, 34(1): 49–57 (in Chinese).

    Google Scholar 

  11. CHEN W B, LI D W, XI Y G, et al. The research of grid-connected inverter control based on decaying amplification aggregation strategy in predictive control [J]. Cluster Computing, 2019, 22(3): 6633–6646.

    Article  Google Scholar 

  12. YANG F, ZHAO X M, JIN H Y, et al. Parameter-free adaptive finite control set model predictive control for PMSM [J/OL]. Proceedings of the CSEE, 2022: 1–9 (in Chinese). https://kns.cnki.net/kcms/detail/11.2107.TM.20220826.1534.006.html

  13. GEBREGERGIS A, ISLAM M, SEBASTIAN T, et al. Evaluation of inductance in a permanent magnet synchronous motor [C]//2011 IEEE International Electric Machines & Drives Conference. Niagara Falls: IEEE, 2011: 1171–1176.

    Chapter  Google Scholar 

  14. KONG Q L. Research on vector control method of permanent magnet synchronous motor based on parameter identification [D]. Chengdu: University of Electronic Science and Technology of China, 2019 (in Chinese).

    Google Scholar 

  15. KIVANC O C, OZTURK S B. Sensorless PMSM drive based on stator feedforward voltage estimation improved with MRAS multiparameter estimation [J]. IEEE/ASME Transactions on Mechatronics, 2018, 23(3): 1326–1337.

    Article  Google Scholar 

  16. JIA C Y, WANG X D, ZHOU K. Model predictive control method for current control of IPMSM with inductance parameter identification [J]. Electric Machines and Control, 2021, 25(11): 75–82 (in Chinese).

    Google Scholar 

  17. LIU F Y, KANG E L, CUI N Z, et al. Single loop predictive control of permanent magnet synchronous motor based on disturbance observation [J]. Electric Drive, 2021, 51(4): 13–21 (in Chinese).

    Google Scholar 

  18. ZHAO K H, CHEN Y, ZHANG C F, et al. Finite-set model predictive fault control for demagnetization faults of permanent magnet synchronous motor drives [J]. Journal of Electronic Measurement and Instrument, 2019, 33(7): 79–87 (in Chinese).

    Google Scholar 

  19. LI L, LIU Y, ZHOU Y H. Review of robust model predictive control technology for permanent magnet synchronous motors [J]. Industrial Instrumentation & Automation, 2020(5): 11–15 (in Chinese).

  20. SHI C W, XIE Z X, CHEN Z Y, et al. Model-free predictive control based on ultra-local model for permanent magnet synchronous machines [J]. Electric Machines and Control, 2021, 25(8): 1–8 (in Chinese).

    Google Scholar 

  21. CHEN Z Y, QIU J Q, JIN M J. Finite control set nonparametric model predictive control for permanent magnet synchronous machines [J]. Electric Machines and Control, 2019, 23(1): 19–26 (in Chinese).

    Google Scholar 

  22. CARLET P G, TINAZZI F, BOLOGNANI S, et al. An effective model-free predictive current control for synchronous reluctance motor drives [J]. IEEE Transactions on Industry Applications, 2019, 55(4): 3781–3790.

    Article  Google Scholar 

  23. YANG L Y, LI D W, LU J B, et al. Robust MPC for constrained uncertain systems with data-driven improvement [C]//2018 37th Chinese Control Conference. Wuhan: IEEE, 2018: 3623–3628.

    Google Scholar 

  24. XI Y G, LI D W, LIN S. Model predictive control — status and challenges [J]. Acta Automatica Sinica, 2013, 39(3): 222–236 (in Chinese).

    Article  MathSciNet  Google Scholar 

  25. XI Y G, LI D W. Fundamental philosophy and status of qualitative synthesis of model predictive control [J]. Acta Automatica Sinica, 2008, 34(10): 1225–1234 (in Chinese).

    Article  MathSciNet  MATH  Google Scholar 

  26. XI Y G. Predictive control [M]. 2nd ed. Beijing: National Defense Industry Press, 2013 (in Chinese).

    Google Scholar 

  27. TANG C. Nonlinear programming problem solution based on Matlab [J]. Computer & Digital Engineering, 2013, 41(7): 1100–1102 (in Chinese).

    Google Scholar 

  28. ZHAO M, LI S Y. Nonlinear model predictive control optimization algorithm based on the trust-region quadratic programming [J]. Control Theory & Applications, 2009, 26(6): 634–640 (in Chinese).

    MathSciNet  MATH  Google Scholar 

  29. HOU Z S, WANG Z. From model-based control to data-driven control: Survey, classification and perspective [J]. Information Sciences, 2013, 235: 3–35.

    Article  MathSciNet  MATH  Google Scholar 

  30. HOU L M, GU H W, JIANG S K, et al. Research on dual-loop predictive control for PMSM fed by fault-tolerant inverter [J]. Computer Engineering and Applications, 2019, 55(18): 212–217 (in Chinese).

    Google Scholar 

  31. CHEN W B. Design and application of aggregation optimization based fast model predictive control for power systems [D]. Shanghai: Shanghai Jiao Tong University, 2020 (in Chinese).

    Google Scholar 

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Authors and Affiliations

  1. School of Railway Transportation, Shanghai Institute of Technology, Shanghai, 201418, China

    Songyang Li  (李松阳), Wenbo Chen  (陈文博) & Heng Wan  (万衡)

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  1. Songyang Li  (李松阳)
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  2. Wenbo Chen  (陈文博)
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  3. Heng Wan  (万衡)
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Correspondence to Wenbo Chen  (陈文博).

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Li, S., Chen, W. & Wan, H. Model Predictive Control Method Based on Data-Driven Approach for Permanent Magnet Synchronous Motor Control System. J. Shanghai Jiaotong Univ. (Sci.) (2023). https://doi.org/10.1007/s12204-023-2600-4

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  • DOI: https://doi.org/10.1007/s12204-023-2600-4

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