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A Novel BSO Algorithm for Three-Layer Neural Network Optimization Applied to UAV Edge Control

Dechao Chen Orcid Logo, Zhaotian Fang, Shuai Li Orcid Logo

Neural Processing Letters, Volume: 55, Issue: 5, Pages: 6733 - 6752

Swansea University Author: Shuai Li Orcid Logo

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DOI (Published version): 10.1007/s11063-023-11157-9

Abstract

Unmanned aerial vehicle is an unmanned aircraft operated by radio remote control equipment and self-provided program control device. The flight control system is the core of the entire flight process of unmanned aerial vehicle to complete take-off, mission execution and recovery. The classical propo...

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Published in: Neural Processing Letters
ISSN: 1370-4621 1573-773X
Published: Springer Science and Business Media LLC 2023
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URI: https://cronfa.swan.ac.uk/Record/cronfa62579
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fullrecord <?xml version="1.0" encoding="utf-8"?><rfc1807 xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:xsd="http://www.w3.org/2001/XMLSchema"><bib-version>v2</bib-version><id>62579</id><entry>2023-02-06</entry><title>A Novel BSO Algorithm for Three-Layer Neural Network Optimization Applied to UAV Edge Control</title><swanseaauthors><author><sid>42ff9eed09bcd109fbbe484a0f99a8a8</sid><ORCID>0000-0001-8316-5289</ORCID><firstname>Shuai</firstname><surname>Li</surname><name>Shuai Li</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2023-02-06</date><deptcode>MECH</deptcode><abstract>Unmanned aerial vehicle is an unmanned aircraft operated by radio remote control equipment and self-provided program control device. The flight control system is the core of the entire flight process of unmanned aerial vehicle to complete take-off, mission execution and recovery. The classical proportion integral differential controller is widely used in the modern control field because of its simple principle and flexible implementation. However, tuning proportion integral differential parameters depends on lots of experience. In this paper, a three-layer feedforward neural network is proposed and used to stabilize the unmanned aerial vehicle edge control system. A novel weight and structure determination method that incorporates bionic beetle swarm optimization algorithm, called beetle swarm optimization weight and structure determination algorithm, is proposed to train the three-layer neural network. Furthermore, the sigmoid activation functions are utilized in the beetle swarm optimization weight and structure determination algorithm to identify the ideal weight and structure of the neural network when dealing with fitting and validation. Then, other five algorithms are added for the comparative analysis, namely particle swarm optimization algorithm, genetic algorithm, bat algorithm, firefly algorithm and artificial bee colony algorithm. In this way, the performance improvement of the beetle swarm optimization algorithm can be highlighted. Finally, the application of unmanned aerial vehicle edge control is given by using two different control methods respectively, i.e., the proportion integral differential controller and the beetle swarm optimization weight and structure determination neural network. It can be seen from the results that the beetle swarm optimization weight and structure determination neural network can enable the unmanned aerial vehicle to better achieve trajectory tracking by showing a better performance on edge control.</abstract><type>Journal Article</type><journal>Neural Processing Letters</journal><volume>55</volume><journalNumber>5</journalNumber><paginationStart>6733</paginationStart><paginationEnd>6752</paginationEnd><publisher>Springer Science and Business Media LLC</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint>1370-4621</issnPrint><issnElectronic>1573-773X</issnElectronic><keywords>Weight and structure determination (WASD) method, Control system, Neural networks, Beetle swarm optimization (BSO), UAV flight control</keywords><publishedDay>1</publishedDay><publishedMonth>10</publishedMonth><publishedYear>2023</publishedYear><publishedDate>2023-10-01</publishedDate><doi>10.1007/s11063-023-11157-9</doi><url>http://dx.doi.org/10.1007/s11063-023-11157-9</url><notes/><college>COLLEGE NANME</college><department>Mechanical Engineering</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>MECH</DepartmentCode><institution>Swansea University</institution><apcterm/><funders>This work was supported in part by the National Natural Science Foundation of China under Grant 62276085 and Grant 61906054, in part by the Natural Science Foundation of Zhejiang Province under Grant LY21-F030006.</funders><projectreference/><lastEdited>2024-01-03T14:58:53.3193503</lastEdited><Created>2023-02-06T08:53:04.3038561</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering</level></path><authors><author><firstname>Dechao</firstname><surname>Chen</surname><orcid>0000-0002-4817-4531</orcid><order>1</order></author><author><firstname>Zhaotian</firstname><surname>Fang</surname><order>2</order></author><author><firstname>Shuai</firstname><surname>Li</surname><orcid>0000-0001-8316-5289</orcid><order>3</order></author></authors><documents/><OutputDurs/></rfc1807>
spelling v2 62579 2023-02-06 A Novel BSO Algorithm for Three-Layer Neural Network Optimization Applied to UAV Edge Control 42ff9eed09bcd109fbbe484a0f99a8a8 0000-0001-8316-5289 Shuai Li Shuai Li true false 2023-02-06 MECH Unmanned aerial vehicle is an unmanned aircraft operated by radio remote control equipment and self-provided program control device. The flight control system is the core of the entire flight process of unmanned aerial vehicle to complete take-off, mission execution and recovery. The classical proportion integral differential controller is widely used in the modern control field because of its simple principle and flexible implementation. However, tuning proportion integral differential parameters depends on lots of experience. In this paper, a three-layer feedforward neural network is proposed and used to stabilize the unmanned aerial vehicle edge control system. A novel weight and structure determination method that incorporates bionic beetle swarm optimization algorithm, called beetle swarm optimization weight and structure determination algorithm, is proposed to train the three-layer neural network. Furthermore, the sigmoid activation functions are utilized in the beetle swarm optimization weight and structure determination algorithm to identify the ideal weight and structure of the neural network when dealing with fitting and validation. Then, other five algorithms are added for the comparative analysis, namely particle swarm optimization algorithm, genetic algorithm, bat algorithm, firefly algorithm and artificial bee colony algorithm. In this way, the performance improvement of the beetle swarm optimization algorithm can be highlighted. Finally, the application of unmanned aerial vehicle edge control is given by using two different control methods respectively, i.e., the proportion integral differential controller and the beetle swarm optimization weight and structure determination neural network. It can be seen from the results that the beetle swarm optimization weight and structure determination neural network can enable the unmanned aerial vehicle to better achieve trajectory tracking by showing a better performance on edge control. Journal Article Neural Processing Letters 55 5 6733 6752 Springer Science and Business Media LLC 1370-4621 1573-773X Weight and structure determination (WASD) method, Control system, Neural networks, Beetle swarm optimization (BSO), UAV flight control 1 10 2023 2023-10-01 10.1007/s11063-023-11157-9 http://dx.doi.org/10.1007/s11063-023-11157-9 COLLEGE NANME Mechanical Engineering COLLEGE CODE MECH Swansea University This work was supported in part by the National Natural Science Foundation of China under Grant 62276085 and Grant 61906054, in part by the Natural Science Foundation of Zhejiang Province under Grant LY21-F030006. 2024-01-03T14:58:53.3193503 2023-02-06T08:53:04.3038561 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering Dechao Chen 0000-0002-4817-4531 1 Zhaotian Fang 2 Shuai Li 0000-0001-8316-5289 3
title A Novel BSO Algorithm for Three-Layer Neural Network Optimization Applied to UAV Edge Control
spellingShingle A Novel BSO Algorithm for Three-Layer Neural Network Optimization Applied to UAV Edge Control
Shuai Li
title_short A Novel BSO Algorithm for Three-Layer Neural Network Optimization Applied to UAV Edge Control
title_full A Novel BSO Algorithm for Three-Layer Neural Network Optimization Applied to UAV Edge Control
title_fullStr A Novel BSO Algorithm for Three-Layer Neural Network Optimization Applied to UAV Edge Control
title_full_unstemmed A Novel BSO Algorithm for Three-Layer Neural Network Optimization Applied to UAV Edge Control
title_sort A Novel BSO Algorithm for Three-Layer Neural Network Optimization Applied to UAV Edge Control
author_id_str_mv 42ff9eed09bcd109fbbe484a0f99a8a8
author_id_fullname_str_mv 42ff9eed09bcd109fbbe484a0f99a8a8_***_Shuai Li
author Shuai Li
author2 Dechao Chen
Zhaotian Fang
Shuai Li
format Journal article
container_title Neural Processing Letters
container_volume 55
container_issue 5
container_start_page 6733
publishDate 2023
institution Swansea University
issn 1370-4621
1573-773X
doi_str_mv 10.1007/s11063-023-11157-9
publisher Springer Science and Business Media LLC
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering
url http://dx.doi.org/10.1007/s11063-023-11157-9
document_store_str 0
active_str 0
description Unmanned aerial vehicle is an unmanned aircraft operated by radio remote control equipment and self-provided program control device. The flight control system is the core of the entire flight process of unmanned aerial vehicle to complete take-off, mission execution and recovery. The classical proportion integral differential controller is widely used in the modern control field because of its simple principle and flexible implementation. However, tuning proportion integral differential parameters depends on lots of experience. In this paper, a three-layer feedforward neural network is proposed and used to stabilize the unmanned aerial vehicle edge control system. A novel weight and structure determination method that incorporates bionic beetle swarm optimization algorithm, called beetle swarm optimization weight and structure determination algorithm, is proposed to train the three-layer neural network. Furthermore, the sigmoid activation functions are utilized in the beetle swarm optimization weight and structure determination algorithm to identify the ideal weight and structure of the neural network when dealing with fitting and validation. Then, other five algorithms are added for the comparative analysis, namely particle swarm optimization algorithm, genetic algorithm, bat algorithm, firefly algorithm and artificial bee colony algorithm. In this way, the performance improvement of the beetle swarm optimization algorithm can be highlighted. Finally, the application of unmanned aerial vehicle edge control is given by using two different control methods respectively, i.e., the proportion integral differential controller and the beetle swarm optimization weight and structure determination neural network. It can be seen from the results that the beetle swarm optimization weight and structure determination neural network can enable the unmanned aerial vehicle to better achieve trajectory tracking by showing a better performance on edge control.
published_date 2023-10-01T14:58:55Z
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score 11.035874

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