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Three Vectors Model Predictive Torque Control Without Weighting Factor Based on Electromagnetic Torque Feedback Compensation

Author

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  • Haixia Li

    (Department of Mechanical and Control Engineering, Guilin University of Technology, Guilin 541000, China)

  • Jican Lin

    (Department of Mechanical and Control Engineering, Guilin University of Technology, Guilin 541000, China)

  • Ziguang Lu

    (College of Electrical Engineering, Guangxi University, Nanning 530004, China)

Abstract

Finite control set-model predictive torque control (FCS-MPTC) depends on the system parameters and the weight coefficients setting. At the same time, since the actual load disturbance is unavoidable, the model parameters are not matched, and there is a torque tracking error. In traditional FCS-MPTC, the outer loop—that is, the speed loop—adopts a classic Proportional Integral (PI) controller, abbreviated as PI-MPTC. The pole placement of the PI controller is usually designed by a plunge-and-test, and it is difficult to achieve optimal dynamic performance and optimal suppression of concentrated disturbances at the same time. Aiming at squirrel cage induction motors, this paper first proposes an outer-loop F-ETFC-MPTC control strategy based on a feed-forward factor for electromagnetic torque feedback compensation (F-ETFC). The electromagnetic torque was imported to the input of the current regulator, which is used as the control input signal of feedback compensation of the speed loop; therefore, the capacity of an anti-load-torque-disturbance of the speed loop was improved. The given speed is quantified by a feed-forward factor into the input of the current regulator, which is used as the feed-forward adjustment control input of the speed controller to improve the dynamic response of the speed loop. The range of the feed-forward factor and feed-back compensation coefficient can be obtained according to the structural analysis of the system, which simplifies the process of parameter design adjustment. At the same time, the multi-objective optimization based on the sorting method replaces the single cost function in traditional control, so that the selection of the voltage vector works without the weight coefficient and can solve complicated calculation problems in traditional control. Finally, according to the relationship between the voltage vector and the switch state, the virtual six groups of three vector voltages can be adjusted in both the direction and amplitude, thereby effectively improving the control performance and reducing the flow rate and torque ripple. The experiment is based on the dSPACE platform, and experimental results verify the feasibility of the proposed F-ETFC-MPTC. Compared with traditional PI-MPTC, the feed-forward factor can effectively improve the stability time of the system by more than 10 percent, electromagnetic torque feedback compensation can improve the anti-load torque disturbance ability of the system by more than 60 percent, and the three-vector voltage method can effectively reduce the disturbance.

Suggested Citation

  • Haixia Li & Jican Lin & Ziguang Lu, 2019. "Three Vectors Model Predictive Torque Control Without Weighting Factor Based on Electromagnetic Torque Feedback Compensation," Energies, MDPI, vol. 12(7), pages 1-19, April.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:7:p:1393-:d:221801
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    References listed on IDEAS

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    1. Jilong Zhao & Xiaowei Quan & Mengdie Jing & Mingyao Lin & Nian Li, 2018. "Design, Analysis and Model Predictive Control of an Axial Field Switched-Flux Permanent Magnet Machine for Electric Vehicle/Hybrid Electric Vehicle Applications," Energies, MDPI, vol. 11(7), pages 1-22, July.
    2. Fengxiang Wang & Zhenbin Zhang & Xuezhu Mei & José Rodríguez & Ralph Kennel, 2018. "Advanced Control Strategies of Induction Machine: Field Oriented Control, Direct Torque Control and Model Predictive Control," Energies, MDPI, vol. 11(1), pages 1-13, January.
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    Cited by:

    1. Zhanqing Zhou & Xin Gu & Zhiqiang Wang & Guozheng Zhang & Qiang Geng, 2019. "An Improved Torque Control Strategy of PMSM Drive Considering On-Line MTPA Operation," Energies, MDPI, vol. 12(15), pages 1-17, July.
    2. Hamdi Echeikh & Mahmoud A. Mossa & Nguyen Vu Quynh & Abdelsalam A. Ahmed & Hassan Haes Alhelou, 2021. "Enhancement of Induction Motor Dynamics Using a Novel Sensorless Predictive Control Algorithm," Energies, MDPI, vol. 14(14), pages 1-28, July.
    3. Shu Xiong & Jian Pan & Yucui Yang, 2022. "Robust Decoupling Vector Control of Interior Permanent Magnet Synchronous Motor Used in Electric Vehicles with Reduced Parameter Mismatch Impacts," Sustainability, MDPI, vol. 14(19), pages 1-16, September.
    4. Zhicheng Liu & Yang Zhao, 2019. "Robust Perturbation Observer-based Finite Control Set Model Predictive Current Control for SPMSM Considering Parameter Mismatch," Energies, MDPI, vol. 12(19), pages 1-18, September.

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