Adaptive Gain Scheduling using Reinforcement Learning for Quadcopter Control

TL;DR

This paper introduces a reinforcement learning-based method to adapt control gains in real-time for quadcopters, significantly improving trajectory tracking accuracy over static gain controllers.

Contribution

It applies reinforcement learning, specifically PPO, to adapt control gains in-flight, demonstrating improved performance over traditional static gain methods.

Findings

Over 40% reduction in tracking error with adaptive gains

Reinforcement learning effectively tunes control parameters in real-time

Adaptive scheme outperforms static gain controllers in trajectory tracking

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.