Artificial Intelligence Based Spacecraft Resilience Optimization in Space Informatics Digital Twins

This article focuses on optimizing the elasticity of spacecraft by harnessing the power of artificial intelligence (AI) technology. With the support of spatial informatics and digital twins technology, this work initially employs AI techniques, specifically the radial basis function neural networks,...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE transactions on aerospace and electronic systems Jg. 61; H. 2; S. 1834 - 1847
Hauptverfasser: Lyu, Zhihan, Guo, Jinkang, Lou, Ranran, Lv, Haibin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.04.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Aerodynamics
Aircraft
Algorithms
Artificial intelligence
Artificial intelligence (AI)
Convergence
Design optimization
Digital twins
Elasticity
Global optimization
Heuristic algorithms
improved particle swarm algorithm
Informatics
Lagrange multiplier
Machine learning
Multiple objective analysis
Neural networks
Optimization
Particle swarm optimization
Radial basis function
Space vehicles
Spacecraft
spacecraft optimization
spatial information
Trajectory
ISSN:0018-9251, 1557-9603, 1557-9603
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Abstract This article focuses on optimizing the elasticity of spacecraft by harnessing the power of artificial intelligence (AI) technology. With the support of spatial informatics and digital twins technology, this work initially employs AI techniques, specifically the radial basis function neural networks, a deep learning algorithm, to perform global optimization and orbit fitting for spacecraft. Augmented Lagrangian multipliers are then introduced to locally optimize this neural network. Additionally, to further enhance the spacecraft's flexibility, an improved particle swarm optimization (PSO) algorithm is applied to optimize the proposed network. The work also introduces a periodic variational multiobjective quantum particle swarm optimization (PMQPSO) algorithm. Subsequently, a rigid-flexible coupled dynamics model for the spacecraft is established, and relevant simulations and experiments are conducted to support this work. The results indicate that the average fitness of the improved PMQPSO algorithm decreases to 18.23 after 500 iterations, with its performance being at least 3.2% higher than that of the classical quantum PSO algorithm. Furthermore, after the initial decline in the first order, the limiter residuals no longer decline and exhibit convergence, as the residual curve transitions from high to low, indicating a gradual improvement in convergence and stability. These findings highlight the advantages of the PMQPSO algorithm in optimizing the spacecraft's elasticity. In conclusion, this parameter optimization holds practical significance for the design optimization of aircraft aerodynamic shapes.
AbstractList This article focuses on optimizing the elasticity of spacecraft by harnessing the power of artificial intelligence (AI) technology. With the support of spatial informatics and digital twins technology, this work initially employs AI techniques, specifically the radial basis function neural networks, a deep learning algorithm, to perform global optimization and orbit fitting for spacecraft. Augmented Lagrangian multipliers are then introduced to locally optimize this neural network. Additionally, to further enhance the spacecraft's flexibility, an improved particle swarm optimization (PSO) algorithm is applied to optimize the proposed network. The work also introduces a periodic variational multiobjective quantum particle swarm optimization (PMQPSO) algorithm. Subsequently, a rigid-flexible coupled dynamics model for the spacecraft is established, and relevant simulations and experiments are conducted to support this work. The results indicate that the average fitness of the improved PMQPSO algorithm decreases to 18.23 after 500 iterations, with its performance being at least 3.2% higher than that of the classical quantum PSO algorithm. Furthermore, after the initial decline in the first order, the limiter residuals no longer decline and exhibit convergence, as the residual curve transitions from high to low, indicating a gradual improvement in convergence and stability. These findings highlight the advantages of the PMQPSO algorithm in optimizing the spacecraft's elasticity. In conclusion, this parameter optimization holds practical significance for the design optimization of aircraft aerodynamic shapes.
Author Lou, Ranran
Lyu, Zhihan
Guo, Jinkang
Lv, Haibin
Author_xml – sequence: 1
  givenname: Zhihan
  orcidid: 0000-0003-2525-3074
  surname: Lyu
  fullname: Lyu, Zhihan
  email: lvzhihan@gmail.com
  organization: Faculty of Arts, Uppsala University, Uppsala, Sweden
– sequence: 2
  givenname: Jinkang
  orcidid: 0000-0003-2633-6314
  surname: Guo
  fullname: Guo, Jinkang
  email: 2020025951@qdu.edu.cn
  organization: College of Computer Science and Technology, Qingdao University, Qingdao, China
– sequence: 3
  givenname: Ranran
  orcidid: 0000-0002-3029-3444
  surname: Lou
  fullname: Lou, Ranran
  email: louranran1113@gmail.com
  organization: College of Computer Science and Technology, Qingdao University, Qingdao, China
– sequence: 4
  givenname: Haibin
  orcidid: 0000-0003-1059-4765
  surname: Lv
  fullname: Lv, Haibin
  email: lvhaibinsoa@gmail.com
  organization: North China Sea Offshore Engineering Survey Institute,Ministry of Natural Resources, North Sea Bureau, Qingdao, China
BackLink https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-556965$$DView record from Swedish Publication Index (Uppsala universitet)
BookMark eNp9kF1L5DAUhsOisKO7P0DYi4K3dsxH07SX47cgCDrrbUgzJ8ORTluTFNn99aZWFvFicxOSvM97wnNA9rq-A0KOGF0yRuvT9erycckpL5aikHWl6m9kwaRUeV1SsUcWlLIqr7lk38lBCM_pWFSFWBC78hEdWjRtdttFaFvcQmchOzMBNtnjYCxYb1zMHiBgi-9v90PEHf41Efsuw25OJdz1fpcubcgucIsxVa5fsQs_yL4zbYCfH_sh-X11uT6_ye_ur2_PV3e5FYLF3JbOSeC1U5wXQgEzlW2g4Wqz4W7TsIqC4o5S45QqLWW2oNY2prGNELwpC3FITube8ArD2OjB4874P7o3qC_waaV7v9XjqKUs61Km-PEcH3z_MkKI-rkffZd-qAWr0pJUTKVsTlnfh-DB_atlVE_q9aReT-r1h_rEqC-MTTYmW9EbbP9L_ppJBIBPk0pVl5yJN1ySlTs
CODEN IEARAX
CitedBy_id crossref_primary_10_1109_ACCESS_2025_3601615
Cites_doi 10.31772/2712-8970-2023-24-2-335-347
10.3390/s23063110
10.1109/JMASS.2021.3069938
10.1016/j.ins.2021.11.052
10.1080/0951192X.2021.1992657
10.2514/1.A35044
10.1109/TNSE.2022.3198818
10.1016/j.iotcps.2022.06.001
10.1007/s11837-020-04388-x
10.1016/j.aei.2023.101876
10.3390/info14050299
10.1016/j.actaastro.2020.10.007
10.1007/s40435-022-01033-0
10.1109/TAES.2021.3060734
10.1007/s42401-020-00069-4
10.1109/ACCESS.2023.3272835
10.1109/TPEL.2020.2994254
10.1109/ACCESS.2020.3015892
10.1186/s10033-022-00760-x
10.3390/s21165568
10.1016/j.jcp.2021.110765
10.2514/1.A34521
10.1109/ACCESS.2020.2993648
10.1109/TR.2020.3001232
10.1007/s00500-021-05799-x
10.1016/j.ast.2019.105527
10.1109/LSP.2020.3021925
10.1177/14613484221114883
10.1109/ACCESS.2019.2947297
10.3390/s21041417
10.1016/j.ymssp.2022.109423
10.1007/s00500-020-04834-7
10.1109/TSMC.2023.3292426
10.1016/j.jweia.2021.104590
10.2514/1.G006101
10.1109/TSC.2023.3242606
10.1007/s12555-022-0295-1
ContentType Journal Article
Copyright Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2025
Copyright_xml – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2025
DBID 97E
RIA
RIE
AAYXX
CITATION
7SP
7TB
8FD
FR3
H8D
L7M
ADTPV
AOWAS
DF2
DOI 10.1109/TAES.2024.3459879
DatabaseName IEEE All-Society Periodicals Package (ASPP) 2005–Present
IEEE All-Society Periodicals Package (ASPP) 1998–Present
IEEE Electronic Library (IEL)
CrossRef
Electronics & Communications Abstracts
Mechanical & Transportation Engineering Abstracts
Technology Research Database
Engineering Research Database
Aerospace Database
Advanced Technologies Database with Aerospace
SwePub
SwePub Articles
SWEPUB Uppsala universitet
DatabaseTitle CrossRef
Aerospace Database
Engineering Research Database
Technology Research Database
Mechanical & Transportation Engineering Abstracts
Advanced Technologies Database with Aerospace
Electronics & Communications Abstracts
DatabaseTitleList
Aerospace Database
Database_xml – sequence: 1
  dbid: RIE
  name: IEEE Electronic Library (IEL)
  url: https://ieeexplore.ieee.org/
  sourceTypes: Publisher
DeliveryMethod fulltext_linktorsrc
Discipline Engineering
EISSN 1557-9603
EndPage 1847
ExternalDocumentID oai_DiVA_org_uu_556965
10_1109_TAES_2024_3459879
10679621
Genre orig-research
GrantInformation_xml – fundername: National Natural Science Foundation of China
  grantid: 61902203
  funderid: 10.13039/501100001809
GroupedDBID -~X
0R~
29I
4.4
41~
5GY
5VS
6IK
97E
AAJGR
AASAJ
AAWTH
ABQJQ
ABVLG
ACGFO
ACIWK
ACNCT
AENEX
AETIX
AGQYO
AGSQL
AHBIQ
AI.
AIBXA
AKJIK
AKQYR
ALLEH
ALMA_UNASSIGNED_HOLDINGS
ASUFR
ATWAV
BEFXN
BFFAM
BGNUA
BKEBE
BPEOZ
CS3
DU5
EBS
EJD
F5P
H~9
IAAWW
IBMZZ
ICLAB
IFIPE
IFJZH
IPLJI
JAVBF
LAI
M43
OCL
P2P
RIA
RIE
RNS
TN5
VH1
AAYXX
CITATION
7SP
7TB
8FD
AARMG
ABAZT
FR3
H8D
L7M
ADTPV
AOWAS
DF2
ID FETCH-LOGICAL-c331t-c6ff5e29f722437e1a8cbeb27dd2fdb180e72f00af776c01c40ccbabcb332b643
IEDL.DBID RIE
ISICitedReferencesCount 1
ISICitedReferencesURI http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001465130000038&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN 0018-9251
1557-9603
IngestDate Tue Nov 04 16:41:27 EST 2025
Tue Jul 22 15:11:53 EDT 2025
Sat Nov 29 07:34:37 EST 2025
Tue Nov 18 22:06:01 EST 2025
Mon Dec 08 03:37:12 EST 2025
IsPeerReviewed true
IsScholarly true
Issue 2
Language English
License https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html
https://doi.org/10.15223/policy-029
https://doi.org/10.15223/policy-037
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c331t-c6ff5e29f722437e1a8cbeb27dd2fdb180e72f00af776c01c40ccbabcb332b643
Notes ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ORCID 0000-0003-2633-6314
0000-0003-1059-4765
0000-0003-2525-3074
0000-0002-3029-3444
PQID 3188885034
PQPubID 85477
PageCount 14
ParticipantIDs ieee_primary_10679621
proquest_journals_3188885034
crossref_primary_10_1109_TAES_2024_3459879
crossref_citationtrail_10_1109_TAES_2024_3459879
swepub_primary_oai_DiVA_org_uu_556965
PublicationCentury 2000
PublicationDate 2025-04-01
PublicationDateYYYYMMDD 2025-04-01
PublicationDate_xml – month: 04
  year: 2025
  text: 2025-04-01
  day: 01
PublicationDecade 2020
PublicationPlace New York
PublicationPlace_xml – name: New York
PublicationTitle IEEE transactions on aerospace and electronic systems
PublicationTitleAbbrev T-AES
PublicationYear 2025
Publisher IEEE
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher_xml – name: IEEE
– name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
References ref13
ref35
ref12
ref34
ref15
ref37
ref14
ref36
ref31
ref30
ref11
ref33
ref10
ref32
ref1
Ivanov (ref2) 2020; 13
ref17
ref16
ref38
ref19
ref18
ref24
ref23
ref26
ref25
ref20
ref22
ref21
ref28
ref27
ref29
ref8
ref7
ref9
ref4
ref3
ref6
ref5
References_xml – ident: ref25
  doi: 10.31772/2712-8970-2023-24-2-335-347
– ident: ref8
  doi: 10.3390/s23063110
– ident: ref12
  doi: 10.1109/JMASS.2021.3069938
– ident: ref18
  doi: 10.1016/j.ins.2021.11.052
– ident: ref22
  doi: 10.1080/0951192X.2021.1992657
– ident: ref1
  doi: 10.2514/1.A35044
– ident: ref26
  doi: 10.1109/TNSE.2022.3198818
– ident: ref5
  doi: 10.1016/j.iotcps.2022.06.001
– ident: ref16
  doi: 10.1007/s11837-020-04388-x
– ident: ref24
  doi: 10.1016/j.aei.2023.101876
– ident: ref20
  doi: 10.3390/info14050299
– ident: ref11
  doi: 10.1016/j.actaastro.2020.10.007
– volume: 13
  start-page: 111
  issue: 1
  year: 2020
  ident: ref2
  article-title: Timeline and problems of wing-in-ground craft building: From Volga-2 motorboat to aerospace systems
  publication-title: Int. J. Control Automat.
– ident: ref10
  doi: 10.1007/s40435-022-01033-0
– ident: ref27
  doi: 10.1109/TAES.2021.3060734
– ident: ref7
  doi: 10.1007/s42401-020-00069-4
– ident: ref9
  doi: 10.1109/ACCESS.2023.3272835
– ident: ref34
  doi: 10.1109/TPEL.2020.2994254
– ident: ref35
  doi: 10.1109/ACCESS.2020.3015892
– ident: ref23
  doi: 10.1186/s10033-022-00760-x
– ident: ref3
  doi: 10.3390/s21165568
– ident: ref4
  doi: 10.1016/j.jcp.2021.110765
– ident: ref28
  doi: 10.2514/1.A34521
– ident: ref32
  doi: 10.1109/ACCESS.2020.2993648
– ident: ref30
  doi: 10.1109/TR.2020.3001232
– ident: ref21
  doi: 10.1007/s00500-021-05799-x
– ident: ref31
  doi: 10.1016/j.ast.2019.105527
– ident: ref33
  doi: 10.1109/LSP.2020.3021925
– ident: ref13
  doi: 10.1177/14613484221114883
– ident: ref6
  doi: 10.1109/ACCESS.2019.2947297
– ident: ref17
  doi: 10.3390/s21041417
– ident: ref38
  doi: 10.1016/j.ymssp.2022.109423
– ident: ref36
  doi: 10.1007/s00500-020-04834-7
– ident: ref14
  doi: 10.1109/TSMC.2023.3292426
– ident: ref15
  doi: 10.1016/j.jweia.2021.104590
– ident: ref29
  doi: 10.2514/1.G006101
– ident: ref19
  doi: 10.1109/TSC.2023.3242606
– ident: ref37
  doi: 10.1007/s12555-022-0295-1
SSID ssj0014843
Score 2.4574573
Snippet This article focuses on optimizing the elasticity of spacecraft by harnessing the power of artificial intelligence (AI) technology. With the support of spatial...
SourceID swepub
proquest
crossref
ieee
SourceType Open Access Repository
Aggregation Database
Enrichment Source
Index Database
Publisher
StartPage 1834
SubjectTerms Aerodynamics
Aircraft
Algorithms
Artificial intelligence
Artificial intelligence (AI)
Convergence
Design optimization
Digital twins
Elasticity
Global optimization
Heuristic algorithms
improved particle swarm algorithm
Informatics
Lagrange multiplier
Machine learning
Multiple objective analysis
Neural networks
Optimization
Particle swarm optimization
Radial basis function
Space vehicles
Spacecraft
spacecraft optimization
spatial information
Trajectory
Title Artificial Intelligence Based Spacecraft Resilience Optimization in Space Informatics Digital Twins
URI https://ieeexplore.ieee.org/document/10679621
https://www.proquest.com/docview/3188885034
https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-556965
Volume 61
WOSCitedRecordID wos001465130000038&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
journalDatabaseRights – providerCode: PRVIEE
  databaseName: IEEE Electronic Library (IEL)
  customDbUrl:
  eissn: 1557-9603
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0014843
  issn: 1557-9603
  databaseCode: RIE
  dateStart: 19650101
  isFulltext: true
  titleUrlDefault: https://ieeexplore.ieee.org/
  providerName: IEEE
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3PS8MwFA46POjB3-J0Sg56EbqlTdu0x-kmCjJFp3gLzS8pzCpu03_flzSO7aBgT4EmpeRL8t6X5H0PoRMRMwGWiAbWOwhipUgg0kwFsEoSYmRBqYuterphg0H2_Jzf-WB1FwujtXaXz3TbFt1ZvnqTU7tV1gndrocNG19mLK2DtWZHBnHmr8iFMIPBavsjzJDknWG3_wBUMIrbNE6AZOcLRshlVVl0MOdFQ52hudz45y9uonXvUeJuPQS20JKuttHanM7gDpL2ZS0Vga_nNDjxOdgwhR-AN4P3WJgJvtfjcuRmO76FxeTVR2nisqprYR--ZMWdca98sSlH8PCrrMa76PGyP7y4Cnx2hUBSGk4CmRqT6Cg3LLKihDosMimAZzOlIqNEmBHNIkNIYaCvJQllTKQUhZCC0kiAI7OHGtVbpfcRBjdFJ0akKbAtoHusKHQY6UwwJWVuctVE5Ke7ufTS4zYDxog7CkJybhHiFiHuEWqis1mT91p346_KuxaJuYo1CE3U-gGV-6k55rCIwZMQGjfRaQ30rKHV2u6VT10OmPLplCdJmqfJwS-fP0Srkc0G7O7xtFBj8jHVR2hFfk7K8cexG53fS-zjIA
linkProvider IEEE
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1NT9wwEB0higQ9FMqH2LKAD_RSKeDESZwcFxYEYrtUZYu4WfEXirQNFbvb_n3GjlkthyKRU6TYUZRne2Y8fm8AjmTKJVoiFjnvIEq1ppHMCx3hKkmpVRVjnlt1N-DDYXF_X_4IZHXPhTHG-MNn5tjd-ly-flQzt1V2EvtdD0cb_-BKZwW61jxpkBbhkFyMcxjtdkhixrQ8GfXObzEYTNJjlmYYZpevzJCvq_LaxVyUDfWm5mL9nR-5AZ-CT0l67SD4DEum2YSPC0qDW6Dcw1YsglwtqHCSU7Rimtxi5Iz-Y2Wn5KeZ1GM_38kNLie_A0-T1E3bigQCk5N3Jv36wRUdIaN_dTPZhl8X56OzyyjUV4gUY_E0Urm1mUlKyxMnS2jiqlASI22udWK1jAtqeGIprSznuaKxSqlSspJKMpZIdGV2YLl5bMwuEHRUTGZlnmO8hQEfryoTJ6aQXCtV2lJ3gL78bqGC-LirgTEWPgihpXAICYeQCAh14Nu8y59WeeOtxtsOiYWGLQgd6L6AKsLknAhcxvDKKEs78LUFet7RqW3367ueQEzFbCayLC_z7Mt_Xn8Iq5ej7wMxuBpe78Fa4moD-1M9XViePs3MPqyov9N68nTgR-ozRDXmaQ
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Artificial+Intelligence+Based+Spacecraft+Resilience+Optimization+in+Space+Informatics+Digital+Twins&rft.jtitle=IEEE+transactions+on+aerospace+and+electronic+systems&rft.au=Lyu%2C+Zhihan&rft.au=Guo%2C+Jinkang&rft.au=Lou%2C+Ranran&rft.au=Lv%2C+Haibin&rft.date=2025-04-01&rft.issn=0018-9251&rft.eissn=1557-9603&rft.volume=61&rft.issue=2&rft.spage=1834&rft.epage=1847&rft_id=info:doi/10.1109%2FTAES.2024.3459879&rft.externalDBID=n%2Fa&rft.externalDocID=10_1109_TAES_2024_3459879
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0018-9251&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0018-9251&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0018-9251&client=summon