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Neural-Networks Control for Hover to High-Speed-Level-Flight Transition of Ducted Fan UAV With Provable Stability

Zihuan Cheng, Hailong Pei, Shuai Li Orcid Logo

IEEE Access, Volume: 8, Pages: 100135 - 100151

Swansea University Author: Shuai Li Orcid Logo

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DOI (Published version): 10.1109/access.2020.2997877

Abstract

In this paper, we focus on the transition control of a ducted fan vertical take-off and landing (VTOL) unmanned aerial vehicle (UAV). To achieve a steady transition from hover to high-speed flight, a neural-networks-based controller is proposed to learn the system dynamics and compensate for the tra...

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Published in: IEEE Access
ISSN: 2169-3536
Published: Institute of Electrical and Electronics Engineers (IEEE) 2020
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URI: https://cronfa.swan.ac.uk/Record/cronfa54501
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spelling 2020-08-16T11:20:45.9471754 v2 54501 2020-06-18 Neural-Networks Control for Hover to High-Speed-Level-Flight Transition of Ducted Fan UAV With Provable Stability 42ff9eed09bcd109fbbe484a0f99a8a8 0000-0001-8316-5289 Shuai Li Shuai Li true false 2020-06-18 MECH In this paper, we focus on the transition control of a ducted fan vertical take-off and landing (VTOL) unmanned aerial vehicle (UAV). To achieve a steady transition from hover to high-speed flight, a neural-networks-based controller is proposed to learn the system dynamics and compensate for the tracking error between the aircraft dynamics and the desired dynamic performance. In prior, we derive the nonlinear system model of the aircraft full-envelope dynamics. Then, we propose a novel neural-networks-based control scheme and apply it on the underactuated aircraft system. Key features of the proposed controller consist of projection operator, state predictor and dynamic-formed adaptive input. It is proved and guaranteed that the tracking errors of both state predictor and neural-networks weights are upper bounded during the whole neural-networks learning procedure. The very adaptive input is formed into a dynamic structure that helps achieve a reliable fast convergence performance of the proposed controller, especially in high-frequency disturbance conditions. Consequently, the closed-loop system of the aircraft is able to track a certain trajectory with desired dynamic performance. Satisfactory results are obtained from both simulations and practical flight test in accomplishing the designed flight course. Journal Article IEEE Access 8 100135 100151 Institute of Electrical and Electronics Engineers (IEEE) 2169-3536 8 6 2020 2020-06-08 10.1109/access.2020.2997877 COLLEGE NANME Mechanical Engineering COLLEGE CODE MECH Swansea University 2020-08-16T11:20:45.9471754 2020-06-18T10:26:27.5486557 Zihuan Cheng 1 Hailong Pei 2 Shuai Li 0000-0001-8316-5289 3 54501__17522__03b777ae565b469db2297bde8d6415f8.pdf 54501.pdf 2020-06-18T10:36:03.9331172 Output 8097794 application/pdf Version of Record true This work is licensed under a Creative Commons Attribution 4.0 License. true eng https://creativecommons.org/licenses/by/4.0/
title Neural-Networks Control for Hover to High-Speed-Level-Flight Transition of Ducted Fan UAV With Provable Stability
spellingShingle Neural-Networks Control for Hover to High-Speed-Level-Flight Transition of Ducted Fan UAV With Provable Stability
Shuai Li
title_short Neural-Networks Control for Hover to High-Speed-Level-Flight Transition of Ducted Fan UAV With Provable Stability
title_full Neural-Networks Control for Hover to High-Speed-Level-Flight Transition of Ducted Fan UAV With Provable Stability
title_fullStr Neural-Networks Control for Hover to High-Speed-Level-Flight Transition of Ducted Fan UAV With Provable Stability
title_full_unstemmed Neural-Networks Control for Hover to High-Speed-Level-Flight Transition of Ducted Fan UAV With Provable Stability
title_sort Neural-Networks Control for Hover to High-Speed-Level-Flight Transition of Ducted Fan UAV With Provable Stability
author_id_str_mv 42ff9eed09bcd109fbbe484a0f99a8a8
author_id_fullname_str_mv 42ff9eed09bcd109fbbe484a0f99a8a8_***_Shuai Li
author Shuai Li
author2 Zihuan Cheng
Hailong Pei
Shuai Li
format Journal article
container_title IEEE Access
container_volume 8
container_start_page 100135
publishDate 2020
institution Swansea University
issn 2169-3536
doi_str_mv 10.1109/access.2020.2997877
publisher Institute of Electrical and Electronics Engineers (IEEE)
document_store_str 1
active_str 0
description In this paper, we focus on the transition control of a ducted fan vertical take-off and landing (VTOL) unmanned aerial vehicle (UAV). To achieve a steady transition from hover to high-speed flight, a neural-networks-based controller is proposed to learn the system dynamics and compensate for the tracking error between the aircraft dynamics and the desired dynamic performance. In prior, we derive the nonlinear system model of the aircraft full-envelope dynamics. Then, we propose a novel neural-networks-based control scheme and apply it on the underactuated aircraft system. Key features of the proposed controller consist of projection operator, state predictor and dynamic-formed adaptive input. It is proved and guaranteed that the tracking errors of both state predictor and neural-networks weights are upper bounded during the whole neural-networks learning procedure. The very adaptive input is formed into a dynamic structure that helps achieve a reliable fast convergence performance of the proposed controller, especially in high-frequency disturbance conditions. Consequently, the closed-loop system of the aircraft is able to track a certain trajectory with desired dynamic performance. Satisfactory results are obtained from both simulations and practical flight test in accomplishing the designed flight course.
published_date 2020-06-08T04:08:04Z
_version_ 1763753574997688320
score 11.036116

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