Reinforcement learning control for multi-axis rotor configuration UAV

Yi Wei Dai, Chen Huan Pi, Kai Chun Hu, Stone Cheng

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

Abstract

This paper proposes an multiusability reinforcement learning controller design method in low-level control of multi-axis rotor configuration unmanned aerial vehicle (UAV). In other reinforcement learning applications, the trained neural network controllers were designed to a model with specific dynamic properties and it usually cannot play a role while the system configuration changed. To fix this problem, we use a six-degree-of-freedom (6-DOF) dynamic model as the prototype of various vehicles to learn the force and moment required for stability during training, and employs a deep neural network directly mapping the states to the actuator commands. The force and moment can be adjusted according to different conditions and applied to different axis multi-rotor. Using reinforcement learning, this paper demonstrate the flight control of quadrotor and hexrotor using trained policy in simulator to present the stability on different multi-rotor structure, and compared the performance with the one previously introduced by trained quadrotor.

Original languageEnglish
Title of host publication2020 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1648-1653
Number of pages6
ISBN (Electronic)9781728167947
DOIs
StatePublished - Jul 2020
Event2020 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2020 - Boston, United States
Duration: 6 Jul 20209 Jul 2020

Publication series

NameIEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM
Volume2020-July

Conference

Conference2020 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2020
CountryUnited States
CityBoston
Period6/07/209/07/20

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