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A Control Method for IPMSM Based on Active Disturbance Rejection Control and Model Predictive Control

Author

Listed:
  • Fang Liu

    (School of Automation, Central South University, Changsha 410083, China
    Hunan Xiangjiang Artificial Intelligence Academy, Changsha 410083, China)

  • Haotian Li

    (School of Automation, Central South University, Changsha 410083, China)

  • Ling Liu

    (Power Construction Corporation of China, Jiangxi Electric Power Design Institute CO., LTD, Nanchang 330096, China)

  • Runmin Zou

    (School of Automation, Central South University, Changsha 410083, China)

  • Kangzhi Liu

    (Department of Electrical and Electronic Engineering, Chiba University, Chiba 2638522, Japan)

Abstract

In this paper, the speed tracking problem of the interior permanent magnet synchronous motor (IPMSM) of an electric vehicle is studied. A cascade speed control strategy based on active disturbance rejection control (ADRC) and a current control strategy based on improved duty cycle finite control set model predictive control (FCSMPC) are proposed, both of which can reduce torque ripple and current ripple as well as the computational burden. First of all, in the linearization process, some nonlinear terms are added into the control signal for voltage compensation, which can reduce the order of the prediction model. Then, the dq-axis currents are selected by maximum torque per ampere (MTPA). Six virtual vectors are employed to FCSMPC, and a novel way to calculate the duty cycle is adopted. Finally, the simulation results show the validity and superiority of the proposed method.

Suggested Citation

  • Fang Liu & Haotian Li & Ling Liu & Runmin Zou & Kangzhi Liu, 2021. "A Control Method for IPMSM Based on Active Disturbance Rejection Control and Model Predictive Control," Mathematics, MDPI, vol. 9(7), pages 1-16, April.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:7:p:760-:d:528285
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    Citations

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    Cited by:

    1. David Sotelo & Antonio Favela-Contreras & Alfonso Avila & Arturo Pinto & Francisco Beltran-Carbajal & Carlos Sotelo, 2022. "A New Software-Based Optimization Technique for Embedded Latency Improvement of a Constrained MIMO MPC," Mathematics, MDPI, vol. 10(15), pages 1-19, July.
    2. Denis Sidorov, 2023. "Preface to “Model Predictive Control and Optimization for Cyber-Physical Systems”," Mathematics, MDPI, vol. 11(4), pages 1-3, February.
    3. 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.

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