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Grey-Wolf-Optimization-Algorithm-Based Tuned P-PI Cascade Controller for Dual-Ball-Screw Feed Drive Systems

Author

Listed:
  • Qi Liu

    (School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, China
    Division of Human Mechanical Systems and Design, Hokkaido University, N13, W8, Kita-ku, Sapporo 060-8628, Hokkaido, Japan)

  • Hong Lu

    (School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, China)

  • Heisei Yonezawa

    (Division of Mechanical and Aerospace Engineering, Hokkaido University, N13, W8, Kita-ku, Sapporo 060-8628, Hokkaido, Japan)

  • Ansei Yonezawa

    (Division of Mechanical and Aerospace Engineering, Hokkaido University, N13, W8, Kita-ku, Sapporo 060-8628, Hokkaido, Japan)

  • Itsuro Kajiwara

    (Division of Mechanical and Aerospace Engineering, Hokkaido University, N13, W8, Kita-ku, Sapporo 060-8628, Hokkaido, Japan)

  • Ben Wang

    (School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, China)

Abstract

Dual-ball-screw feed drive systems (DBSFDSs) are designed for most high-end manufacturing equipment. However, the mismatch between the dynamic characteristic parameters (e.g., stiffness and inertia) and the P-PI cascade control method reduces the accuracy of the DBSFDSs owing to the structural characteristic changes in the motion. Moreover, the parameters of the P-PI cascade controller of the DBSFDSs are always the same even though the two axes have different dynamic characteristics, and it is difficult to tune two-axis parameters simultaneously. A new application of the combination of the grey wolf optimization (GWO) algorithm and the P-PI cascade controller is presented to solve these problems and enhance the motion performance of DBSFDSs. The novelty is that the flexible coupling model and dynamic stiffness obtained from the motor current can better represent the two-axis coupling dynamic characteristics, and the GWO algorithm is used to adjust the P-PI controller parameters to address variations in the positions of the moving parts and reflect characteristic differences between the two axes. Comparison of simulation and experimental results validated the superiority of the proposed controller over existing ones in practical applications, showing a decrease in the tracking error of the tool center and non-synchronization error of over 34% and 39%, respectively.

Suggested Citation

  • Qi Liu & Hong Lu & Heisei Yonezawa & Ansei Yonezawa & Itsuro Kajiwara & Ben Wang, 2023. "Grey-Wolf-Optimization-Algorithm-Based Tuned P-PI Cascade Controller for Dual-Ball-Screw Feed Drive Systems," Mathematics, MDPI, vol. 11(10), pages 1-29, May.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:10:p:2259-:d:1144966
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    References listed on IDEAS

    as
    1. Mikulas Huba & Damir Vrancic, 2022. "Tuning of PID Control for the Double Integrator Plus Dead Time Model by Modified Real Dominant Pole and Performance Portrait Methods," Mathematics, MDPI, vol. 10(6), pages 1-25, March.
    2. Xuelin Zhang & Xiaobin Xu & Xiaojian Xu & Pingzhi Hou & Haibo Gao & Feng Ma, 2023. "Intelligent Adaptive PID Control for the Shaft Speed of a Marine Electric Propulsion System Based on the Evidential Reasoning Rule," Mathematics, MDPI, vol. 11(5), pages 1-23, February.
    3. Bhargav Bhatt & Himanshu Sharma & Krishan Arora & Gyanendra Prasad Joshi & Bhanu Shrestha, 2023. "Levy Flight-Based Improved Grey Wolf Optimization: A Solution for Various Engineering Problems," Mathematics, MDPI, vol. 11(7), pages 1-31, April.
    4. Mohammad Soleimani Amiri & Rizauddin Ramli & Mohd Faisal Ibrahim & Dzuraidah Abd Wahab & Norazam Aliman, 2020. "Adaptive Particle Swarm Optimization of PID Gain Tuning for Lower-Limb Human Exoskeleton in Virtual Environment," Mathematics, MDPI, vol. 8(11), pages 1-16, November.
    5. Xiaogang Yang & Mengxing Huang & Yuanyuan Wu & Siling Feng, 2023. "Observer-Based PID Control Protocol of Positive Multi-Agent Systems," Mathematics, MDPI, vol. 11(2), pages 1-23, January.
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