A simulation-based machine learning approach for flight control system design of agile maneuvering multicopters

Samet Uzun, Berkay Akbiyik, Burak Yuksek, Mustafa Umut Demirezen, Gokhan Inalhan

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

1 Citation (Scopus)

Abstract

Ability to do agile maneuvers provides an enhanced autonomy level for operations in dense and dynamically changing environments involving urban or nap-of-the-earth flights. In this work, we consider the design of agile maneuvering flight control system through a hybrid machine learning environment which includes both real flight testing and simulation-based deep learning. The main aim is to be able to identify the highly non-linear dynamical characteristics and control patterns that a pilot exploits (in many cases as second-nature) and utilizes while performing agile maneuvers. Specifically, the in-house developed high performance multicopters and their models are used for performing agile maneuvers under pilot control. Later this data is used in designing agile maneuvering outer loop controller designs via deep learning as to mimic (and even enhance) pilot’s agile maneuvering capability on a quadrotor. The control design that we consider is a cascaded flight control system design which consists of classical attitude control loops and neural network outer loops. In the first use case, we considered the pilot controls the velocity of the quadrotor by using a classical attitude control system and input/output data is collected on a data logger. Then, the collected data is used to train a neural network based velocity controller and system performance is evaluated in a test case. In the second use case, we considered, lazy eight maneuver and racetrack patterns are built in the Gazebo environment, which contains several gates. The pilot performs manual flights in this patterns and tries to fly through the defined gates. In these flights, required input/output dataset is collected and it is used to train a neural network-based guidance algorithm, which is developed for autonomous maneuvers. In the third use case, we considered a new racetrack pattern is built within the Gazebo environment, which is not used in the data collecting process of the second use case. Then, the proposed neural network based guidance algorithm is evaluated in the new racetrack pattern without performing any data collecting and training process. The results show that a) the quadrotor can even do maneuvers for which does not specifically exist within its training set, and b) the quadrotor can perform autonomous agile maneuvers at high speeds that are not demonstrated by classical control methods.

Original languageEnglish
Title of host publicationAIAA Scitech 2019 Forum
PublisherAmerican Institute of Aeronautics and Astronautics Inc, AIAA
ISBN (Print)9781624105784
DOIs
Publication statusPublished - 2019
EventAIAA Scitech Forum, 2019 - San Diego, United States
Duration: 7 Jan 201911 Jan 2019

Publication series

NameAIAA Scitech 2019 Forum

Conference

ConferenceAIAA Scitech Forum, 2019
Country/TerritoryUnited States
CitySan Diego
Period7/01/1911/01/19

Bibliographical note

Publisher Copyright:
© 2019 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.

Funding

Dr. Mustafa Umut Demirezen is supported in part by Boeing Faculty Endownment administered by ITU Aerospace Research Center. The authors would like to thank Mevlut Uzun, Tolga Ok, Mustafa Demir and Yusuf Demiroglu for their valuable supports and comments.

FundersFunder number
ITU Aerospace Research Center
Boeing
International Technological University

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