Deterministic Policy Gradient with Integral Compensator for Robust Quadrotor Control
Document Type
Article
Date of Original Version
10-1-2020
Abstract
In this paper, a deep reinforcement learning-based robust control strategy for quadrotor helicopters is proposed. The quadrotor is controlled by a learned neural network which directly maps the system states to control commands in an end-to-end style. The learning algorithm is developed based on the deterministic policy gradient algorithm. By introducing an integral compensator to the actor-critic structure, the tracking accuracy and robustness have been greatly enhanced. Moreover, a two-phase learning protocol which includes both offline and online learning phase is proposed for practical implementation. An offline policy is first learned based on a simplified quadrotor model. Then, the policy is online optimized in actual flight. The proposed approach is evaluated in the flight simulator. The results demonstrate that the offline learned policy is highly robust to model errors and external disturbances. It also shows that the online learning could significantly improve the control performance.
Publication Title, e.g., Journal
IEEE Transactions on Systems, Man, and Cybernetics: Systems
Volume
50
Issue
10
Citation/Publisher Attribution
Wang, Yuanda, Jia Sun, Haibo He, and Changyin Sun. "Deterministic Policy Gradient with Integral Compensator for Robust Quadrotor Control." IEEE Transactions on Systems, Man, and Cybernetics: Systems 50, 10 (2020): 3713-3725. doi: 10.1109/TSMC.2018.2884725.