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Deep-Reinforcement-Learning-Based Active Disturbance Rejection Control for Lateral Path Following of Parafoil System

Author

Listed:
  • Yuemin Zheng

    (College of Artificial Intelligence, Nankai University, Tianjin 300350, China)

  • Jin Tao

    (College of Artificial Intelligence, Nankai University, Tianjin 300350, China
    Silo AI, 00100 Helsinki, Finland)

  • Qinglin Sun

    (College of Artificial Intelligence, Nankai University, Tianjin 300350, China)

  • Hao Sun

    (College of Artificial Intelligence, Nankai University, Tianjin 300350, China)

  • Zengqiang Chen

    (College of Artificial Intelligence, Nankai University, Tianjin 300350, China
    Key Laboratory of Intelligent Robotics of Tianjin, Nankai University, Tianjin 300350, China)

  • Mingwei Sun

    (College of Artificial Intelligence, Nankai University, Tianjin 300350, China)

  • Feng Duan

    (College of Artificial Intelligence, Nankai University, Tianjin 300350, China)

Abstract

The path-following control of the parafoil system is essential for executing missions, such as accurate homing and delivery. In this paper, the lateral path-following control of the parafoil system is studied. First, considering the relative motion between the parafoil canopy and the payload, an eight-degree-of-freedom (DOF) model of the parafoil system is constructed. Then, a guidance law containing the position deviation and heading angle deviation is proposed. Moreover, a linear active disturbance rejection controller (LADRC) is designed based on the guidance law to allow the parafoil system to track the desired path under internal unmodeled dynamics or external environmental disturbances. For the adaptive tuning of the controller parameters, a deep Q-network (DQN) is applied to the LADRC-based path-following control system, and the controller parameters can be adjusted in real time according to the system’s states. Finally, the effectiveness of the proposed method is applied to a parafoil system following circular and straight paths in an environment with wind disturbances. The simulation results show that the proposed method is an effective means to realize the lateral path-following control of the parafoil system, and it can also promote the development of intelligent controllers.

Suggested Citation

  • Yuemin Zheng & Jin Tao & Qinglin Sun & Hao Sun & Zengqiang Chen & Mingwei Sun & Feng Duan, 2022. "Deep-Reinforcement-Learning-Based Active Disturbance Rejection Control for Lateral Path Following of Parafoil System," Sustainability, MDPI, vol. 15(1), pages 1-18, December.
    • Handle: RePEc:gam:jsusta:v:15:y:2022:i:1:p:435-:d:1016532
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    References listed on IDEAS

    as
    1. Ronghui Li & Tieshan Li & Renxiang Bu & Qinling Zheng & C. L. Philip Chen, 2013. "Active Disturbance Rejection with Sliding Mode Control Based Course and Path Following for Underactuated Ships," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-9, November.
    2. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
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    Cited by:

    1. Hamza Assia & Houari Merabet Boulouiha & William David Chicaiza & Juan Manuel Escaño & Abderrahmane Kacimi & José Luis Martínez-Ramos & Mouloud Denai, 2023. "Wind Turbine Active Fault Tolerant Control Based on Backstepping Active Disturbance Rejection Control and a Neurofuzzy Detector," Energies, MDPI, vol. 16(14), pages 1-22, July.
    2. Yuemin Zheng & Jin Tao & Qinglin Sun & Hao Sun & Zengqiang Chen & Mingwei Sun, 2023. "Adaptive Active Disturbance Rejection Load Frequency Control for Power System with Renewable Energies Using the Lyapunov Reward-Based Twin Delayed Deep Deterministic Policy Gradient Algorithm," Sustainability, MDPI, vol. 15(19), pages 1-25, October.

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