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A Novel Moment of Inertia Identification Strategy for Permanent Magnet Motor System Based on Integral Chain Differentiator and Kalman Filter

Author

Listed:
  • Chenchen Jing

    (School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China)

  • Yan Yan

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Shiyu Lin

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Le Gao

    (Weichai Power Co. Ltd., Weifang 261061, China)

  • Zhixin Wang

    (Weichai Power Co. Ltd., Weifang 261061, China)

  • Tingna Shi

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

Abstract

In a motor control system, the parameters tuning of speed and position controller depend on the value of the moment of inertia. A new moment of inertia identification scheme for permanent magnet motor system was proposed in this paper. This is an extension of the existing acceleration deceleration methods, which solves the large moment of inertia identification error caused by variable angular acceleration, large calculation error of inertia torque, and large measurement noise in the acceleration process. Based on the fact that the angular acceleration is not constant and the sampling signal is noisy, the integral chain differentiator was used to calculate the instantaneous angular acceleration at any time and suppress the sampling signal noise at the same time. The error function with instantaneous angular acceleration and inertia torque as parameters was designed to estimate the moment of inertia. In order to calculate the inertia torque accurately, viscous friction torque was considered in the calculation of inertia torque, and Kalman filter was used to estimate the total load torque to solve the problem of under rank of motor motion equation. Simulation and experimental results showed that the proposed method could effectively identify the moment of inertia in both noisy and noiseless environments.

Suggested Citation

  • Chenchen Jing & Yan Yan & Shiyu Lin & Le Gao & Zhixin Wang & Tingna Shi, 2020. "A Novel Moment of Inertia Identification Strategy for Permanent Magnet Motor System Based on Integral Chain Differentiator and Kalman Filter," Energies, MDPI, vol. 14(1), pages 1-23, December.
    • Handle: RePEc:gam:jeners:v:14:y:2020:i:1:p:166-:d:472650
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    References listed on IDEAS

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    1. Ming Yang & Zirui Liu & Jiang Long & Wanying Qu & Dianguo Xu, 2018. "An Algorithm for Online Inertia Identification and Load Torque Observation via Adaptive Kalman Observer-Recursive Least Squares," Energies, MDPI, vol. 11(4), pages 1-17, March.
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