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Improvement of PMSM Sensorless Control Based on Synergetic and Sliding Mode Controllers Using a Reinforcement Learning Deep Deterministic Policy Gradient Agent

Author

Listed:
  • Marcel Nicola

    (Research and Development Department, National Institute for Research, Development and Testing in Electrical Engineering—ICMET Craiova, 200746 Craiova, Romania)

  • Claudiu-Ionel Nicola

    (Research and Development Department, National Institute for Research, Development and Testing in Electrical Engineering—ICMET Craiova, 200746 Craiova, Romania
    Department of Automatic Control and Electronics, University of Craiova, 200585 Craiova, Romania)

  • Dan Selișteanu

    (Department of Automatic Control and Electronics, University of Craiova, 200585 Craiova, Romania)

Abstract

The field-oriented control (FOC) strategy of a permanent magnet synchronous motor (PMSM) in a simplified form is based on PI-type controllers. In addition to their low complexity (an advantage for real-time implementation), these controllers also provide limited performance due to the nonlinear character of the description equations of the PMSM model under the usual conditions of a relatively wide variation in the load torque and the high dynamics of the PMSM speed reference. Moreover, a number of significant improvements in the performance of PMSM control systems, also based on the FOC control strategy, are obtained if the controller of the speed control loop uses sliding mode control (SMC), and if the controllers for the inner control loops of i d and i q currents are of the synergetic type. Furthermore, using such a control structure, very good performance of the PMSM control system is also obtained under conditions of parametric uncertainties and significant variations in the combined rotor-load moment of inertia and the load resistance. To improve the performance of the PMSM control system without using controllers having a more complicated mathematical description, the advantages provided by reinforcement learning (RL) for process control can also be used. This technique does not require the exact knowledge of the mathematical model of the controlled system or the type of uncertainties. The improvement in the performance of the PMSM control system based on the FOC-type strategy, both when using simple PI-type controllers or in the case of complex SMC or synergetic-type controllers, is achieved using the RL based on the Deep Deterministic Policy Gradient (DDPG). This improvement is obtained by using the correction signals provided by a trained reinforcement learning agent, which is added to the control signals u d , u q , and i qref . A speed observer is also implemented for estimating the PMSM rotor speed. The PMSM control structures are presented using the FOC-type strategy, both in the case of simple PI-type controllers and complex SMC or synergetic-type controllers, and numerical simulations performed in the MATLAB/Simulink environment show the improvements in the performance of the PMSM control system, even under conditions of parametric uncertainties, by using the RL-DDPG.

Suggested Citation

  • Marcel Nicola & Claudiu-Ionel Nicola & Dan Selișteanu, 2022. "Improvement of PMSM Sensorless Control Based on Synergetic and Sliding Mode Controllers Using a Reinforcement Learning Deep Deterministic Policy Gradient Agent," Energies, MDPI, vol. 15(6), pages 1-30, March.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:6:p:2208-:d:773608
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    References listed on IDEAS

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    1. Ming-Shyan Wang & Tse-Ming Tsai, 2017. "Sliding Mode and Neural Network Control of Sensorless PMSM Controlled System for Power Consumption and Performance Improvement," Energies, MDPI, vol. 10(11), pages 1-15, November.
    2. Chih-Hong Lin, 2020. "Permanent-Magnet Synchronous Motor Drive System Using Backstepping Control with Three Adaptive Rules and Revised Recurring Sieved Pollaczek Polynomials Neural Network with Reformed Grey Wolf Optimizat," Energies, MDPI, vol. 13(22), pages 1-33, November.
    3. Mengting Ye & Tingna Shi & Huimin Wang & Xinmin Li & Changliang Xia, 2019. "Sensorless-MTPA Control of Permanent Magnet Synchronous Motor Based on an Adaptive Sliding Mode Observer," Energies, MDPI, vol. 12(19), pages 1-15, October.
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    5. Sandra Eriksson, 2019. "Design of Permanent-Magnet Linear Generators with Constant-Torque-Angle Control for Wave Power," Energies, MDPI, vol. 12(7), pages 1-19, April.
    6. Fardila Mohd Zaihidee & Saad Mekhilef & Marizan Mubin, 2019. "Robust Speed Control of PMSM Using Sliding Mode Control (SMC)—A Review," Energies, MDPI, vol. 12(9), pages 1-27, May.
    7. Yuan-Chih Chang & Chi-Ting Tsai & Yong-Lin Lu, 2019. "Current Control of the Permanent-Magnet Synchronous Generator Using Interval Type-2 T-S Fuzzy Systems," Energies, MDPI, vol. 12(15), pages 1-12, July.
    8. Marcel Nicola & Claudiu-Ionel Nicola, 2021. "Fractional-Order Control of Grid-Connected Photovoltaic System Based on Synergetic and Sliding Mode Controllers," Energies, MDPI, vol. 14(2), pages 1-25, January.
    9. Shuang Wang & Jianfei Zhao & Tingzhang Liu & Minqi Hua, 2019. "Adaptive Robust Control System for Axial Flux Permanent Magnet Synchronous Motor of Electric Medium Bus Based on Torque Optimal Distribution Method," Energies, MDPI, vol. 12(24), pages 1-17, December.
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    Cited by:

    1. Claudiu-Ionel Nicola & Marcel Nicola, 2023. "Improved Performance for PMSM Sensorless Control Based on the LADRC Controller, ESO-Type Observer, DO-Type Observer, and RL-TD3 Agent," Mathematics, MDPI, vol. 11(15), pages 1-25, July.
    2. Yongjie Yang & Xudong Liu, 2022. "A Novel Nonsingular Terminal Sliding Mode Observer-Based Sensorless Control for Electrical Drive System," Mathematics, MDPI, vol. 10(17), pages 1-16, August.
    3. Di Liu & Junwei Cao & Mingshuang Liu, 2022. "Joint Optimization of Energy Storage Sharing and Demand Response in Microgrid Considering Multiple Uncertainties," Energies, MDPI, vol. 15(9), pages 1-20, April.
    4. Yanfei Cao & Shuxin Xiao & Zhichen Lin, 2022. "A Flying Restart Strategy for Position Sensorless PMSM Driven by Quasi-Z-Source Inverter," Energies, MDPI, vol. 15(9), pages 1-15, May.
    5. Marcel Nicola & Claudiu-Ionel Nicola, 2022. "Improvement of Linear and Nonlinear Control for PMSM Using Computational Intelligence and Reinforcement Learning," Mathematics, MDPI, vol. 10(24), pages 1-34, December.
    6. Yoon-Seong Lee & Kyoung-Min Choo & Won-Sang Jeong & Chang-Hee Lee & Junsin Yi & Chung-Yuen Won, 2023. "A Virtual Impedance-Based Flying Start Considering Transient Characteristics for Permanent Magnet Synchronous Machine Drive Systems," Energies, MDPI, vol. 16(3), pages 1-17, January.

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