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Improvement of Linear and Nonlinear Control for PMSM Using Computational Intelligence and Reinforcement Learning

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)

Abstract

Starting from the nonlinear operating equations of the permanent magnet synchronous motor (PMSM) and from the global strategy of the field-oriented control (FOC), this article compares the linear and nonlinear control of a PMSM. It presents the linear quadratic regulator (LQR) algorithm as a linear control algorithm, in addition to that obtained through feedback linearization (FL). Naturally, the nonlinear approach through the Lyapunov and Hamiltonian functions leads to results that are superior to those of the linear algorithms. With the particle swarm optimization (PSO), simulated annealing (SA), genetic algorithm (GA), and gray wolf Optimization (GWO) computational intelligence (CI) algorithms, the performance of the PMSM–control system (CS) was optimized by obtaining parameter vectors from the control algorithms by optimizing specific performance indices. Superior performance of the PMSM–CS was also obtained by using reinforcement learning (RL) algorithms, which provided correction command signals (CCSs) after the training stages. Starting from the PMSM–CS performance that was obtained for a benchmark, there were four types of linear and nonlinear control algorithms for the control of a PMSM, together with the means of improving the PMSM–CS performance by using CI algorithms and RL–twin delayed deep deterministic policy gradient (TD3) agent algorithms. The article also presents experimental results that confirm the superiority of PMSM–CS–CI over classical PI-type controllers.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:24:p:4667-:d:998231
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    References listed on IDEAS

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    2. 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.
    3. Gerardo Humberto Valencia-Rivera & Luis Ramon Merchan-Villalba & Guillermo Tapia-Tinoco & Jose Merced Lozano-Garcia & Mario Alberto Ibarra-Manzano & Juan Gabriel Avina-Cervantes, 2020. "Hybrid LQR-PI Control for Microgrids under Unbalanced Linear and Nonlinear Loads," Mathematics, MDPI, vol. 8(7), pages 1-25, July.
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    Keywords

    PMSM; FL; PCH; CI; RL;
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