Adaptive Gain Scheduling using Reinforcement Learning for Quadcopter Control

• URL: http://arxiv.org/abs/2403.07216v1
• Date: Tue, 12 Mar 2024 00:08:54 GMT
• Title: Adaptive Gain Scheduling using Reinforcement Learning for Quadcopter Control
• Authors: Mike Timmerman, Aryan Patel, Tim Reinhart
• Abstract summary: The paper presents a technique using reinforcement learning to adapt the control gains of a quadcopter controller. The primary goal of this controller is to minimize tracking error while following a specified trajectory. The results show that the adaptive gain scheme achieves over 40$%$ decrease in tracking error as compared to the static gain controller.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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