A reinforcement learning (RL) based approach is proposed for PID controller fine-tuning and parameter estimation for effective and accurate tracking of a helix trajectory considering realistic flight controller sampling times. RL exploits a Deep Deterministic Policy Gradient (DDPG) algorithm, which is an off-policy actor-critic method. The quadrotor model follows the Newton-Euler formulation and accounts for complete gyroscopic and drag effects. Training and simulation studies are performed using Matlab/Simulink. Performance evaluation and comparison studies are detailed between the hand-tuned, RL-based tuned, and RL-based full estimation of parameters approaches. Results show that full estimation of controller parameters achieves the smallest attitude and position errors, and that both RL-based strategies significantly improve tracking performance compared to the hand-tuned approach.


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    Title :

    Reinforcement Learning Based PID Parameter Tuning and Estimation for Multirotor UAVs


    Contributors:


    Publication date :

    2024-06-04


    Size :

    3548881 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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