The control performance of model-based control methods depends on the accuracy of the system model. However, due to unknown environmental disturbances, power consumption, etc., quadrotor dynamics are variable during real flight, and real-time updating of the dynamics is required to avoid degradation of control performance due to model errors. This paper proposes an RL-augmented MPC controller by combining MPC and reinforcement learning (RL) with Gaussian process (GP) dynamics for a quadrotor flying in unknown winds. The MPC controller uses a model identified from offline flight data, while the model-based RL controller learns online from real-time flight data. RL controller outputs additional actions to compensate for MPC model errors, while MPC can ensure safe exploration in RL. We design a priority data selection criterion to keep a small dataset to balance model accuracy and computational time. The proposed hybrid controller achieves accurate trajectory tracking under unknown wind disturbance with a maximum speed of 10 m/s.


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    Title :

    Enhancing Quadrotor Control in Wind Disturbances Using Hybrid RL-Augmented MPC


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Yan, Liang (editor) / Duan, Haibin (editor) / Deng, Yimin (editor) / Wang, Mengyun (author) / Liu, Yunzhuo (author) / Niu, Yifeng (author)

    Conference:

    International Conference on Guidance, Navigation and Control ; 2024 ; Changsha, China August 09, 2024 - August 11, 2024



    Publication date :

    2025-03-08


    Size :

    13 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English