Time-attenuating Twin Delayed DDPG for Quadrotor Tracking Control

Continuous trajectory tracking control of quadrotors is challenging when considering noise from the environment. Due to the difficulty in modeling the environmental dynamics, tracking methodologies based on conventional control theory, such as model predictive control, have limitations on tracking a...

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Bibliographic Details
Published in2023 42nd Chinese Control Conference (CCC) pp. 2323 - 2328
Main Authors Deng, Boyuan, Sun, Jian, Li, Zhuo, Wang, Gang
Format Conference Proceeding
LanguageEnglish
Published Technical Committee on Control Theory, Chinese Association of Automation 24.07.2023
Subjects
deep reinforcement learning
Predictive models
quadrotor
Reinforcement learning
Simulation
Stability analysis
Time factors
Training
Trajectory tracking
trajectory tracking control
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Summary:Continuous trajectory tracking control of quadrotors is challenging when considering noise from the environment. Due to the difficulty in modeling the environmental dynamics, tracking methodologies based on conventional control theory, such as model predictive control, have limitations on tracking accuracy and response time. We propose a time-attenuating twin delayed DDPG, a model-free algorithm that is robust to noise, to better handle the trajectory tracking task. A deep reinforcement learning framework is constructed, where a time decay strategy is designed to avoid trapping into local optima. The experimental results show that the tracking error is significantly small, and the operation time is one-tenth of that of a traditional algorithm. The OpenAI Mujoco[1] tool is used to verify the proposed algorithm, and the simulation results show that, the proposed method can significantly improve the training efficiency and effectively improve the accuracy and convergence stability.
ISSN:2161-2927
DOI:10.23919/CCC58697.2023.10241100