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An Algorithm for Online Inertia Identification and Load Torque Observation via Adaptive Kalman Observer-Recursive Least Squares

Author

Listed:
  • Ming Yang

    (Institute of Power Electronics and Electrical Drives, Harbin Institute of Technology, Harbin 150001, China)

  • Zirui Liu

    (Institute of Power Electronics and Electrical Drives, Harbin Institute of Technology, Harbin 150001, China)

  • Jiang Long

    (Institute of Power Electronics and Electrical Drives, Harbin Institute of Technology, Harbin 150001, China)

  • Wanying Qu

    (Institute of Power Electronics and Electrical Drives, Harbin Institute of Technology, Harbin 150001, China)

  • Dianguo Xu

    (Institute of Power Electronics and Electrical Drives, Harbin Institute of Technology, Harbin 150001, China)

Abstract

In this paper, an on-line parameter identification algorithm to iteratively compute the numerical values of inertia and load torque is proposed. Since inertia and load torque are strongly coupled variables due to the degenerate-rank problem, it is hard to estimate relatively accurate values for them in the cases such as when load torque variation presents or one cannot obtain a relatively accurate priori knowledge of inertia. This paper eliminates this problem and realizes ideal online inertia identification regardless of load condition and initial error. The algorithm in this paper integrates a full-order Kalman Observer and Recursive Least Squares, and introduces adaptive controllers to enhance the robustness. It has a better performance when iteratively computing load torque and moment of inertia. Theoretical sensitivity analysis of the proposed algorithm is conducted. Compared to traditional methods, the validity of the proposed algorithm is proved by simulation and experiment results.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jeners:v:11:y:2018:i:4:p:778-:d:138538
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    Citations

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    Cited by:

    1. 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.
    2. Qi Wang & Haitao Yu & Min Wang & Xinbo Qi, 2018. "A Novel Adaptive Neuro-Control Approach for Permanent Magnet Synchronous Motor Speed Control," Energies, MDPI, vol. 11(9), pages 1-21, September.
    3. Tao Liu & Qiaoling Tong & Qiao Zhang & Qidong Li & Linkai Li & Zhaoxuan Wu, 2018. "A Method to Improve the Response of a Speed Loop by Using a Reduced-Order Extended Kalman Filter," Energies, MDPI, vol. 11(11), pages 1-16, October.

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