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

Kai Zhao, Jia Song, Yunlong Hu, Xiaowei Xu and Yang Liu
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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

Mathematics, 2022, vol. 10, issue 15, 1-15

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.

Keywords: reinforcement learning; deep deterministic policy gradient; active disturbance rejection control; quadrotor ummanned aerial vehicle (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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