Adaptive Gain Scheduling using Reinforcement Learning for Quadcopter Control

2024-03-12 · Mike Timmerman, Aryan Patel, Tim Reinhart

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.

📄 PDF Abstract BibTeX arXiv:2403.07216

Code (1)

MikeTimmerman-ae/RL_Drone_Control 공식 구현 pytorch

Tasks

reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)Scheduling

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