Deep reinforcement learning based aggressive flight trajectory tracker

Omar Shadeed, Mehmet Hasanzade, Emre Koyuncu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

In this work, we aim to develop a deep reinforcement learning-based trajectory tracking controller for aerial vehicles based on the Proximal Policy Optimization (PPO) algorithm. The main goal is to minimize the error between the reference position and velocity from the trajectory and the aerial vehicle position and velocity at time t. We used an aerial vehicle dynamic model with PI for the attitude controller and PID for the attitude rate controller. A deep reinforcement learning agent is utilized for generating pitch and roll references to track a desired agile trajectory. Simulation results are conducted to compare our proposed solution with LQR and LQI based trajectory tracking controllers. Our approach has been applied to Crazyflie quadcopter to track the given agile trajectories with acceleration up to 6.2 m/s2, while keeping the root-mean-square tracking error down to 5 cm.

Original languageEnglish
Title of host publicationAIAA Scitech 2021 Forum
PublisherAmerican Institute of Aeronautics and Astronautics Inc, AIAA
Pages1-11
Number of pages11
ISBN (Print)9781624106095
Publication statusPublished - 2021
EventAIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2021 - Virtual, Online
Duration: 11 Jan 202115 Jan 2021

Publication series

NameAIAA Scitech 2021 Forum
Volume1 PartF

Conference

ConferenceAIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2021
CityVirtual, Online
Period11/01/2115/01/21

Bibliographical note

Publisher Copyright:
© 2021, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

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