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Robust Self-Learning PID Control of an Aircraft Anti-Skid Braking System

Author

Listed:
  • Fengrui Xu

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

  • Xuelin Liang

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

  • Mengqiao Chen

    (College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410083, China
    Laboratory of Science and Technology on Integrated Logistics Support, National University of Defense Technology, Changsha 410083, China)

  • Wensheng Liu

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

Abstract

In order to deal with strong nonlinearity and external interference in the braking process, this paper proposes a robust self-learning PID algorithm based on particle swarm optimization, which does not depend on a precise mathematical model of the controlled object. The self-learning function is used to adapt to the diversity of the runway road surface friction, the particle swarm algorithm is used to optimize the rate of self-learning, and robust control is used to deal with the modeling uncertainty and external disturbance of the system. The convergence of the control strategy is proved by theoretical analysis and simulation experiments. The superiority and accuracy of the method are verified by NASA ground test results. The simulation results shows that the adverse effect of the external disturbance is suppressed, and the ideal trajectory is tracked.

Suggested Citation

  • Fengrui Xu & Xuelin Liang & Mengqiao Chen & Wensheng Liu, 2022. "Robust Self-Learning PID Control of an Aircraft Anti-Skid Braking System," Mathematics, MDPI, vol. 10(8), pages 1-18, April.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:8:p:1290-:d:792768
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    References listed on IDEAS

    as
    1. He, Hongwen & Wang, Chen & Jia, Hui & Cui, Xing, 2020. "An intelligent braking system composed single-pedal and multi-objective optimization neural network braking control strategies for electric vehicle," Applied Energy, Elsevier, vol. 259(C).
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