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Runge-Kutta Model Predictive Speed Control for Permanent Magnet Synchronous Motors

Author

Listed:
  • Adile Akpunar

    (Department of Electronics and Computer Education, Pamukkale University, 20160 Denizli, Turkey)

  • Serdar Iplikci

    (Department of Electrical and Electronics Engineering, Pamukkale University, 20160 Denizli, Turkey)

Abstract

Permanent magnet synchronous motors (PMSMs) have commonly been used in a wide spectrum ranging from industry to home appliances because of their advantages over their conventional counterparts. However, PMSMs are multiple-input multiple-output (MIMO) systems with nonlinear dynamics, which makes their control relatively difficult. In this study, a novel model predictive control mechanism, which is referred to as the Runge-Kutta model predictive control (RKMPC), has been applied for speed control of a commercial permanent magnet synchronous motor. Furthermore, the RKMPC method has been utilized for the adaptation of the speed of the motor under load variations via RKMPC-based online parameter estimation. The superiority of RKMPC is that it can take the constraints on the inputs and outputs of the system into consideration, thereby handling the speed and current control in a single loop. It has been shown in the study that the RKMPC mechanism can also estimate the load changes and unknown load disturbances to eliminate their undesired effects for a desirable control accuracy. The performance of the employed mechanism has been tested on a 0.4 kW PMSM motor experimentally for different conditions and compared to the conventional Proportional Integral (PI) method. The tests have shown the efficiency of RKMPC for PMSMs.

Suggested Citation

  • Adile Akpunar & Serdar Iplikci, 2020. "Runge-Kutta Model Predictive Speed Control for Permanent Magnet Synchronous Motors," Energies, MDPI, vol. 13(5), pages 1-17, March.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:5:p:1216-:d:329307
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    References listed on IDEAS

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    1. Hyeong-Jun Yoo & Thai-Thanh Nguyen & Hak-Man Kim, 2019. "MPC with Constant Switching Frequency for Inverter-Based Distributed Generations in Microgrid Using Gradient Descent," Energies, MDPI, vol. 12(6), pages 1-14, March.
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    Cited by:

    1. Mengmeng Tian & Hailiang Cai & Wenliang Zhao & Jie Ren, 2023. "Nonlinear Predictive Control of Interior Permanent Magnet Synchronous Machine with Extra Current Constraint," Energies, MDPI, vol. 16(2), pages 1-14, January.
    2. Yang Liu & Jin Zhao & Quan Yin, 2021. "Model-Based Predictive Rotor Field-Oriented Angle Compensation for Induction Machine Drives," Energies, MDPI, vol. 14(8), pages 1-13, April.

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