# Adaptive Gain Scheduling using Reinforcement Learning for Quadcopter Control

arXiv: https://arxiv.org/abs/2403.07216 · HTML: https://mlsanity.com/paper/2403.07216/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods.
- Builds on 1 of its 7 references that are in the map.
- First posted on arXiv 12 Mar 2024.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 1 of 1)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)

## Built on by (most cited first, 1 of 1)

- [AI-Driven approach for sustainable extraction of earth's subsurface renewable energy while minimizing seismic activity](https://mlsanity.com/paper/2408.03664/) (2024)

## Abstract (arXiv)

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.