Aggressive and robust low-level control and trajectory tracking for quadrotors with deep reinforcement learning

Executing accurate trajectory tracking tasks using a high-performance low-level controller is crucial for quadrotors to be applied in various scenarios, especially those involving uncertain disturbances. However, due to the uncertainties in disturbed environments, developing effective low-level cont...

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Vydáno v:	Neural computing & applications Ročník 37; číslo 3; s. 1223 - 1240
Hlavní autoři:	Chen, Shiyu, Li, Yanjie, Lou, Yunjiang, Lin, Ke
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London Springer London 01.01.2025 Springer Nature B.V
Témata:	Accuracy Artificial Intelligence Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Control algorithms Control methods Controllers Data Mining and Knowledge Discovery Deep learning Disturbances Hovering Image Processing and Computer Vision Initial conditions Methods Original Article Probability and Statistics in Computer Science Proportional integral derivative Robotics Robust control Rotary wing aircraft Simulation Tracking Unmanned aerial vehicles Velocity Vibration control Robustness Trajectory tracking Low-level control Reinforcement learning Quadrotor
ISSN:	0941-0643, 1433-3058
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Popis
Shrnutí:	Executing accurate trajectory tracking tasks using a high-performance low-level controller is crucial for quadrotors to be applied in various scenarios, especially those involving uncertain disturbances. However, due to the uncertainties in disturbed environments, developing effective low-level controllers with traditional model-based control schemes is challenging. This paper presents an aggressive and robust reinforcement learning (RL)-based low-level control policy for quadrotors. The policy maps the observed quadrotor state directly to motor thrust commands, without requiring the quadrotor dynamics. Additionally, a trajectory generation pipeline is developed to improve the accuracy of trajectory tracking tasks based on differential flatness. With the learned low-level control policy, extensive simulations and real-world experiments are implemented to validate the performance of the policy. The results indicate that our RL-based low-level control policy outperforms traditional proportional–integral–derivative (PID) control methods and related learning-based policies in terms of accuracy and robustness, particularly in environments with uncertain disturbances. Furthermore, the proposed RL-based control policy exhibits an aggressive response in trajectory tracking, even when the speed of the desired trajectory is increased to 6 m/s. Moreover, the learned policy demonstrates strong vibration suppression capabilities and enables the quadrotor to recover to a hovering state from random initial conditions with shorter response time.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ISSN:	0941-0643 1433-3058
DOI:	10.1007/s00521-024-10675-4