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A Method to Improve the Response of a Speed Loop by Using a Reduced-Order Extended Kalman Filter

Author

Listed:
  • Tao Liu

    (School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Qiaoling Tong

    (School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Qiao Zhang

    (School of Automation, Wuhan University of Technology, Wuhan 430074, China)

  • Qidong Li

    (School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Linkai Li

    (School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Zhaoxuan Wu

    (School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China)

Abstract

In servo systems, encoders are usually used to measure the position and speed signals of electric machines. But in a low speed range, the traditional M/T method has a larger time delay, which will cause an increase of the speed loop order and degradation of the speed loop performance. A method employed to reduce the delay of speed feedback by using a reduced-order Extended Kalman Filter (EKF) is introduced in this paper. The speed of the permanent magnet synchronous motor is estimated by the reduced-order EKF in a low speed range, which reduces the delay of speed feedback and extends the cutoff frequency of the speed loop to improve the dynamic performance of the servo system. In order to solve the issues that the traditional full-order EKF is sensitive to the inertia of the system and computationally complex, a composite load torque observer (CLTO) is proposed in this paper. The load torque and the friction torque are simultaneously observed by the CLTO. Additionally, the CLTO is used to reduce the order of the EKF, which reduces the sensitivity of EKF on inertia to enhance the robustness of the algorithm and simplifies the computational complexity. The feasibility and effectivity of the above method are verified by simulations and experiments.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:11:p:2886-:d:177947
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    References listed on IDEAS

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    1. Bo Xu & Fangqiang Mu & Guoding Shi & Wei Ji & Huangqiu Zhu, 2016. "State Estimation of Permanent Magnet Synchronous Motor Using Improved Square Root UKF," Energies, MDPI, vol. 9(7), pages 1-14, June.
    2. Xin Wang & Edwin E. Yaz, 2014. "Stochastically resilient extended Kalman filtering for discrete-time nonlinear systems with sensor failures," International Journal of Systems Science, Taylor & Francis Journals, vol. 45(7), pages 1393-1401, July.
    3. Joon B. Park & Xin Wang, 2018. "Sensorless Direct Torque Control of Surface-Mounted Permanent Magnet Synchronous Motors with Nonlinear Kalman Filtering," Energies, MDPI, vol. 11(4), pages 1-19, April.
    4. 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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    Cited by:

    1. Claudiu-Ionel Nicola & Marcel Nicola, 2023. "Improved Performance for PMSM Sensorless Control Based on the LADRC Controller, ESO-Type Observer, DO-Type Observer, and RL-TD3 Agent," Mathematics, MDPI, vol. 11(15), pages 1-25, July.

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