Adaptive Gain Scheduling using Reinforcement Learning for Quadcopter Control
<|reference_start|>Adaptive Gain Scheduling using Reinforcement Learning for Quadcopter Control: The paper presents a technique using reinforcement learning (RL) to adapt the control gains of a quadcopter controller. Specifically, we employed Proximal Policy Optimization (PPO) to train a policy which adapts the gains of a cascaded feedback controller in-flight. The primary goal of this controller is to minimize tracking error while following a specified trajectory. The paper's key objective is to analyze the effectiveness of the adaptive gain policy and compare it to the performance of a static gain control algorithm, where the Integral Squared Error and Integral Time Squared Error are used as metrics. The results show that the adaptive gain scheme achieves over 40$\%$ decrease in tracking error as compared to the static gain controller.<|reference_end|>
@article{timmerman2024adaptive,
    title={Adaptive Gain Scheduling using Reinforcement Learning for Quadcopter
  Control},
    author={Mike Timmerman, Aryan Patel, Tim Reinhart},
    journal={arXiv preprint arXiv:2403.07216},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.07216},
    primaryClass={eess.SY cs.LG cs.RO cs.SY}
}