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Deep Deterministic Policy Gradient-Based Active Disturbance Rejection Controller for Quad-Rotor UAVs

Author

Listed:
  • Kai Zhao

    (School of Astronautics, Beihang University (BUAA), Beijing 100191, China)

  • Jia Song

    (School of Astronautics, Beihang University (BUAA), Beijing 100191, China)

  • Yunlong Hu

    (School of Astronautics, Beihang University (BUAA), Beijing 100191, China)

  • Xiaowei Xu

    (School of Astronautics, Beihang University (BUAA), Beijing 100191, China)

  • Yang Liu

    (School of Automation Science and Electrical Engineering, Beihang University (BUAA), Beijing 100191, China)

Abstract

Thanks to their hovering and vertical take-off and landing abilities, quadrotor unmanned aerial vehicles (UAVs) are receiving a great deal of attention. With the diversified development of the functions of UAVs, the requirements for flight performance with higher stability and maneuverability are increasing. Aiming at parameter uncertainty and external disturbance, a deep deterministic policy gradient-based active disturbance rejection controller (DDPG-ADRC) is proposed. The total disturbances can be compensated dynamically by adjusting the controller bandwidth and the estimation of system parameters online. The tradeoff between anti-interference and rapidity can be better realized in this way compared with the traditional ADRC. The process of parameter tuning is demonstrated through the simulation results of tracking step instruction and sine sweep under ideal and disturbance conditions. Further analysis shows the proposed DDPG-ADRC has better performance.

Suggested Citation

  • Kai Zhao & Jia Song & Yunlong Hu & Xiaowei Xu & Yang Liu, 2022. "Deep Deterministic Policy Gradient-Based Active Disturbance Rejection Controller for Quad-Rotor UAVs," Mathematics, MDPI, vol. 10(15), pages 1-15, July.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:15:p:2686-:d:875670
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    References listed on IDEAS

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    1. Jonas Degrave & Federico Felici & Jonas Buchli & Michael Neunert & Brendan Tracey & Francesco Carpanese & Timo Ewalds & Roland Hafner & Abbas Abdolmaleki & Diego de las Casas & Craig Donner & Leslie F, 2022. "Magnetic control of tokamak plasmas through deep reinforcement learning," Nature, Nature, vol. 602(7897), pages 414-419, February.
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