Adaptive Neural Network Control of a Flexible Spacecraft Subject to Input Nonlinearity and Asymmetric Output Constraint
This article focuses on the vibration reducing and angle tracking problems of a flexible unmanned spacecraft system subject to input nonlinearity, asymmetric output constraint, and system parameter uncertainties. Using the backstepping technique, a boundary control scheme is designed to suppress the...
Saved in:
| Published in | IEEE Transactions on Neural Networks and Learning Systems Vol. 33; no. 11; pp. 6226 - 6234 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
IEEE
01.11.2022
Institute of Electrical and Electronics Engineers (IEEE) The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
Cover
Loading…
| Abstract | This article focuses on the vibration reducing and angle tracking problems of a flexible unmanned spacecraft system subject to input nonlinearity, asymmetric output constraint, and system parameter uncertainties. Using the backstepping technique, a boundary control scheme is designed to suppress the vibration and regulate the angle of the spacecraft. A modified asymmetric barrier Lyapunov function is utilized to ensure that the output constraint is never transgressed. Considering the system robustness, neural networks are used to handle the system parameter uncertainties and compensate for the effect of input nonlinearity. With the proposed adaptive neural network control law, the stability of the closed-loop system is proved based on the Lyapunov analysis, and numerical simulations are carried out to show the validity of the developed control scheme. |
|---|---|
| AbstractList | This article focuses on the vibration reducing and angle tracking problems of a flexible unmanned spacecraft system subject to input nonlinearity, asymmetric output constraint, and system parameter uncertainties. Using the backstepping technique, a boundary control scheme is designed to suppress the vibration and regulate the angle of the spacecraft. A modified asymmetric barrier Lyapunov function is utilized to ensure that the output constraint is never transgressed. Considering the system robustness, neural networks are used to handle the system parameter uncertainties and compensate for the effect of input nonlinearity. With the proposed adaptive neural network control law, the stability of the closed-loop system is proved based on the Lyapunov analysis, and numerical simulations are carried out to show the validity of the developed control scheme. This article focuses on the vibration reducing and angle tracking problems of a flexible unmanned spacecraft system subject to input nonlinearity, asymmetric output constraint, and system parameter uncertainties. Using the backstepping technique, a boundary control scheme is designed to suppress the vibration and regulate the angle of the spacecraft. A modified asymmetric barrier Lyapunov function is utilized to ensure that the output constraint is never transgressed. Considering the system robustness, neural networks are used to handle the system parameter uncertainties and compensate for the effect of input nonlinearity. With the proposed adaptive neural network control law, the stability of the closed-loop system is proved based on the Lyapunov analysis, and numerical simulations are carried out to show the validity of the developed control scheme.This article focuses on the vibration reducing and angle tracking problems of a flexible unmanned spacecraft system subject to input nonlinearity, asymmetric output constraint, and system parameter uncertainties. Using the backstepping technique, a boundary control scheme is designed to suppress the vibration and regulate the angle of the spacecraft. A modified asymmetric barrier Lyapunov function is utilized to ensure that the output constraint is never transgressed. Considering the system robustness, neural networks are used to handle the system parameter uncertainties and compensate for the effect of input nonlinearity. With the proposed adaptive neural network control law, the stability of the closed-loop system is proved based on the Lyapunov analysis, and numerical simulations are carried out to show the validity of the developed control scheme. |
| Author | Liu, Yu Chen, Xiongbin Yokoi, Hiroshi Wu, Yilin Cai, He |
| Author_xml | – sequence: 1 givenname: Yu orcidid: 0000-0002-4191-5974 surname: Liu fullname: Liu, Yu email: auylau@scut.edu.cn organization: School of Automation Science and Engineering, South China University of Technology, Guangzhou, China – sequence: 2 givenname: Xiongbin surname: Chen fullname: Chen, Xiongbin email: aucxb@mail.scut.edu.cn organization: School of Automation Science and Engineering, South China University of Technology, Guangzhou, China – sequence: 3 givenname: Yilin orcidid: 0000-0003-1721-1931 surname: Wu fullname: Wu, Yilin email: lyw@gdei.edu.cn organization: Department of Computer Science, Guangdong University of Education, Guangzhou, China – sequence: 4 givenname: He orcidid: 0000-0002-0411-1774 surname: Cai fullname: Cai, He email: caihe@scut.edu.cn organization: School of Automation Science and Engineering, South China University of Technology, Guangzhou, China – sequence: 5 givenname: Hiroshi surname: Yokoi fullname: Yokoi, Hiroshi email: yokoi@mce.uec.ac.jp organization: Department of Mechanical Engineering and Intelligent Systems, The University of Electro-Communications, Chofu, Japan |
| BackLink | https://cir.nii.ac.jp/crid/1872553967815468544$$DView record in CiNii https://www.ncbi.nlm.nih.gov/pubmed/33999824$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9kc1u1DAUhS1UREvpC4CELNEFmxn8mzjL0YhCpdF0MUViZznOjeQhsYPjUObt6zDTLrrAC18vvnN8dc5bdOaDB4TeU7KklFRf7rfbzW7JCKNLTkpWkfIVumC0YAvGlTp7fpc_z9HVOO5JPgWRhajeoHPOq6pSTFygh1VjhuT-AN7CFE2XR3oI8RdeB59i6HBoscE3Hfx1dQd4NxgLNpo24d1U78EmnAK-9cOU8Db4znkw0aUDNr7Bq_HQ95Cis_huSjOSTccUjfPpHXrdmm6Eq9O8RD9uvt6vvy82d99u16vNwgpRpIVSeemyBSkbYgGASCUJsW3ZcNOSqiGsVrw0VQMNZQURxKiaClEWwOrGcMMv0eej7xDD7wnGpHs3Wug64yFMo2aSKUUFJTyjn16g-zBFn7fTLAdc8EIykqmPJ2qqe2j0EF1v4kE_RZoBdQRsDOMYodXWJZPcnKdxnaZEzwXqfwXquUB9KjBL2Qvpk_t_RddHkXcufzXfVJVMSl4VpaJSFEqKea0PR8zlGJ99K8GZIpQ_AqzRsLs |
| CODEN | ITNNAL |
| CitedBy_id | crossref_primary_10_1007_s11071_024_10015_0 crossref_primary_10_3934_mbe_2023906 crossref_primary_10_1109_TFUZZ_2024_3410269 crossref_primary_10_1109_TFUZZ_2022_3199573 crossref_primary_10_1007_s12555_023_0104_5 crossref_primary_10_1016_j_oceaneng_2024_120020 crossref_primary_10_1109_TASE_2023_3340332 crossref_primary_10_1109_TSMC_2023_3283492 crossref_primary_10_1109_TNNLS_2022_3157950 crossref_primary_10_1002_asjc_2767 crossref_primary_10_1109_ACCESS_2024_3474301 crossref_primary_10_1109_TII_2023_3316222 crossref_primary_10_1109_TFUZZ_2024_3442308 crossref_primary_10_1109_JAS_2023_123213 crossref_primary_10_1007_s11071_022_07888_4 crossref_primary_10_1016_j_ijnonlinmec_2024_104914 crossref_primary_10_1007_s12555_022_0707_2 crossref_primary_10_1109_TFUZZ_2024_3375449 crossref_primary_10_1016_j_engappai_2023_107503 crossref_primary_10_1016_j_automatica_2024_111815 crossref_primary_10_1109_TFUZZ_2023_3328030 crossref_primary_10_1109_TFUZZ_2024_3485098 crossref_primary_10_1109_TAES_2024_3367964 crossref_primary_10_1109_JAS_2022_106040 crossref_primary_10_1016_j_ymssp_2024_111640 crossref_primary_10_1109_TCST_2021_3104648 crossref_primary_10_1016_j_ast_2023_108640 crossref_primary_10_1016_j_jfranklin_2024_01_040 crossref_primary_10_1109_TAES_2024_3358778 crossref_primary_10_1007_s11424_023_2312_8 crossref_primary_10_1007_s11071_024_10809_2 crossref_primary_10_1109_TSMC_2024_3492053 crossref_primary_10_1109_TMECH_2021_3126686 crossref_primary_10_1109_TSMC_2022_3149283 crossref_primary_10_1109_TFUZZ_2023_3293194 crossref_primary_10_1631_FITEE_2300679 crossref_primary_10_1109_TFUZZ_2024_3466218 crossref_primary_10_1016_j_ejcon_2023_100780 crossref_primary_10_1002_asjc_3265 crossref_primary_10_1016_j_ast_2024_109244 crossref_primary_10_1007_s13344_024_0023_0 crossref_primary_10_1109_TFUZZ_2022_3164512 crossref_primary_10_1007_s12555_021_0181_2 crossref_primary_10_1109_TIE_2022_3227288 crossref_primary_10_1109_TAES_2022_3183550 crossref_primary_10_1109_TAI_2023_3347182 crossref_primary_10_1109_TNNLS_2023_3263946 crossref_primary_10_1007_s11633_022_1346_z crossref_primary_10_1109_TSMC_2024_3449343 crossref_primary_10_1016_j_ejcon_2024_100970 crossref_primary_10_1016_j_ast_2024_109093 crossref_primary_10_1177_10775463241275720 crossref_primary_10_1016_j_oceaneng_2022_113001 crossref_primary_10_1016_j_ast_2023_108504 crossref_primary_10_1109_TSMC_2022_3221734 crossref_primary_10_1080_00207179_2025_2473676 crossref_primary_10_1016_j_cnsns_2025_108697 crossref_primary_10_1109_TFUZZ_2022_3165634 crossref_primary_10_1109_TFUZZ_2024_3455939 crossref_primary_10_1007_s11071_022_07980_9 crossref_primary_10_1109_TFUZZ_2024_3489719 crossref_primary_10_1109_TASE_2023_3326347 crossref_primary_10_1016_j_asr_2024_07_004 crossref_primary_10_1016_j_ast_2023_108470 crossref_primary_10_1109_LRA_2024_3405386 crossref_primary_10_1109_TMECH_2023_3287915 crossref_primary_10_1007_s11071_024_10063_6 crossref_primary_10_1109_TCYB_2021_3055151 crossref_primary_10_1109_TFUZZ_2022_3164536 crossref_primary_10_1002_oca_2952 crossref_primary_10_1007_s11432_024_4293_3 crossref_primary_10_1109_TIE_2023_3269481 crossref_primary_10_3390_asi5040061 crossref_primary_10_1016_j_jfranklin_2024_107395 crossref_primary_10_1002_rnc_7634 crossref_primary_10_1007_s12555_022_0675_6 crossref_primary_10_1002_rnc_7355 crossref_primary_10_1080_00207179_2024_2442734 crossref_primary_10_1016_j_neucom_2024_127382 crossref_primary_10_1002_asjc_3231 crossref_primary_10_1016_j_jfranklin_2023_09_035 crossref_primary_10_1016_j_ins_2023_120014 crossref_primary_10_1109_TFUZZ_2024_3365072 crossref_primary_10_1002_rnc_6723 crossref_primary_10_1080_00207721_2025_2480203 crossref_primary_10_1007_s11071_022_07784_x crossref_primary_10_1109_TCYB_2024_3524242 crossref_primary_10_1109_TFUZZ_2024_3434690 crossref_primary_10_1109_TMECH_2023_3235054 crossref_primary_10_1080_00207721_2024_2411039 crossref_primary_10_1002_acs_3633 crossref_primary_10_1016_j_nahs_2023_101405 crossref_primary_10_1109_TMECH_2024_3359334 crossref_primary_10_1109_JAS_2023_123093 crossref_primary_10_1016_j_conengprac_2023_105627 crossref_primary_10_1007_s11071_023_08389_8 crossref_primary_10_1109_JSTSP_2024_3430822 crossref_primary_10_1016_j_jfranklin_2023_07_035 crossref_primary_10_1109_TFUZZ_2024_3418772 crossref_primary_10_1177_10775463221124640 |
| Cites_doi | 10.1109/TII.2018.2873175 10.1109/TIE.2016.2530789 10.1109/TSMC.2020.2999485 10.1016/j.automatica.2017.07.028 10.1007/s11432-019-2893-y 10.1109/CCA.2013.6662773 10.1109/TSMC.2018.2882734 10.1109/TIE.2018.2884172 10.1109/TSMC.2019.2944900 10.1109/TCYB.2016.2581173 10.1109/TNNLS.2019.2952175 10.1109/TSMC.2018.2843523 10.1109/TIE.2017.2694391 10.1109/CDC.2005.1583507 10.1109/TMECH.2017.2721553 10.1016/j.automatica.2014.10.117 10.1109/TCST.2017.2780055 10.1109/TAC.2015.2471815 10.1016/j.automatica.2011.08.044 10.1109/TFUZZ.2015.2486811 10.1109/TCYB.2019.2923461 10.1109/ACCESS.2018.2805800 10.1109/TSMC.2016.2562506 10.1109/TMECH.2019.2951812 10.1109/TCST.2008.2012166 10.1109/TFUZZ.2018.2882173 10.1109/TFUZZ.2018.2826475 10.1007/s11432-019-2714-7 10.1109/TIE.2015.2432107 10.1109/TCYB.2017.2667680 10.1109/TFUZZ.2017.2761323 10.1109/TSMC.2015.2504552 10.1109/TNNLS.2017.2665581 10.1109/TCYB.2021.3055151 10.1109/TMECH.2020.2975567 10.1109/TIE.2019.2931230 10.1109/TIE.2019.2946554 10.1109/TNNLS.2018.2872595 10.1016/j.automatica.2014.09.006 10.1016/j.isatra.2016.04.006 10.1109/TCYB.2015.2456028 10.1016/j.automatica.2016.11.002 10.1109/TMECH.2020.2987963 10.1109/TNN.2010.2047115 10.1109/TCYB.2018.2808321 10.1007/BF00940460 10.1007/BF00934533 10.1109/TAC.2009.2020638 10.1109/TIE.2015.2421884 10.1109/TAC.2018.2874746 |
| ContentType | Journal Article |
| Copyright | Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022 |
| Copyright_xml | – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022 |
| DBID | 97E RIA RIE RYH AAYXX CITATION NPM 7QF 7QO 7QP 7QQ 7QR 7SC 7SE 7SP 7SR 7TA 7TB 7TK 7U5 8BQ 8FD F28 FR3 H8D JG9 JQ2 KR7 L7M L~C L~D P64 7X8 |
| DOI | 10.1109/TNNLS.2021.3072907 |
| DatabaseName | IEEE All-Society Periodicals Package (ASPP) 2005–Present IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CiNii Complete CrossRef PubMed Aluminium Industry Abstracts Biotechnology Research Abstracts Calcium & Calcified Tissue Abstracts Ceramic Abstracts Chemoreception Abstracts Computer and Information Systems Abstracts Corrosion Abstracts Electronics & Communications Abstracts Engineered Materials Abstracts Materials Business File Mechanical & Transportation Engineering Abstracts Neurosciences Abstracts Solid State and Superconductivity Abstracts METADEX Technology Research Database ANTE: Abstracts in New Technology & Engineering Engineering Research Database Aerospace Database Materials Research Database ProQuest Computer Science Collection Civil Engineering Abstracts Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Biotechnology and BioEngineering Abstracts MEDLINE - Academic |
| DatabaseTitle | CrossRef PubMed Materials Research Database Technology Research Database Computer and Information Systems Abstracts – Academic Mechanical & Transportation Engineering Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Materials Business File Aerospace Database Engineered Materials Abstracts Biotechnology Research Abstracts Chemoreception Abstracts Advanced Technologies Database with Aerospace ANTE: Abstracts in New Technology & Engineering Civil Engineering Abstracts Aluminium Industry Abstracts Electronics & Communications Abstracts Ceramic Abstracts Neurosciences Abstracts METADEX Biotechnology and BioEngineering Abstracts Computer and Information Systems Abstracts Professional Solid State and Superconductivity Abstracts Engineering Research Database Calcium & Calcified Tissue Abstracts Corrosion Abstracts MEDLINE - Academic |
| DatabaseTitleList | Materials Research Database MEDLINE - Academic PubMed |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: RIE name: IEEE Electronic Library (IEL) url: https://proxy.k.utb.cz/login?url=https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 2162-2388 |
| EndPage | 6234 |
| ExternalDocumentID | 33999824 10_1109_TNNLS_2021_3072907 9432801 |
| Genre | orig-research Journal Article |
| GrantInformation_xml | – fundername: Guangdong Basic and Applied Basic Research Foundation grantid: 2020B1515120071 – fundername: National Natural Science Foundation of China grantid: 61203060 funderid: 10.13039/501100001809 – fundername: Fundamental Research Funds for the Central Universities of SCUT grantid: 2020ZYGXZR059 funderid: 10.13039/501100012226 – fundername: Science and Technology Planning Project of Guangdong Province grantid: 2019A050510015 funderid: 10.13039/501100012245 |
| GroupedDBID | 0R~ 4.4 5VS 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACIWK ACPRK AENEX AFRAH AGQYO AGSQL AHBIQ AKJIK AKQYR ALMA_UNASSIGNED_HOLDINGS ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ EBS EJD IFIPE IPLJI JAVBF M43 MS~ O9- OCL PQQKQ RIA RIE RNS RYH AAYXX CITATION RIG NPM 7QF 7QO 7QP 7QQ 7QR 7SC 7SE 7SP 7SR 7TA 7TB 7TK 7U5 8BQ 8FD F28 FR3 H8D JG9 JQ2 KR7 L7M L~C L~D P64 7X8 |
| ID | FETCH-LOGICAL-c446t-880607fe55d0ceee058500cf7d3af09d02b837a9ded126040a8b14476e2bda3a3 |
| IEDL.DBID | RIE |
| ISSN | 2162-237X 2162-2388 |
| IngestDate | Fri Jul 11 16:55:41 EDT 2025 Mon Jun 30 07:07:03 EDT 2025 Thu Apr 03 07:00:51 EDT 2025 Tue Jul 01 00:27:39 EDT 2025 Thu Apr 24 23:12:00 EDT 2025 Thu Jun 26 22:41:37 EDT 2025 Wed Aug 27 02:14:42 EDT 2025 |
| IsPeerReviewed | false |
| IsScholarly | true |
| Issue | 11 |
| 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-c446t-880607fe55d0ceee058500cf7d3af09d02b837a9ded126040a8b14476e2bda3a3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ORCID | 0000-0002-4191-5974 0000-0002-0411-1774 0000-0003-1721-1931 |
| PMID | 33999824 |
| PQID | 2729636520 |
| PQPubID | 85436 |
| PageCount | 9 |
| ParticipantIDs | pubmed_primary_33999824 nii_cinii_1872553967815468544 ieee_primary_9432801 proquest_miscellaneous_2528814103 crossref_citationtrail_10_1109_TNNLS_2021_3072907 crossref_primary_10_1109_TNNLS_2021_3072907 proquest_journals_2729636520 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2022-11-01 |
| PublicationDateYYYYMMDD | 2022-11-01 |
| PublicationDate_xml | – month: 11 year: 2022 text: 2022-11-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | United States |
| PublicationPlace_xml | – name: United States – name: Piscataway |
| PublicationTitle | IEEE Transactions on Neural Networks and Learning Systems |
| PublicationTitleAbbrev | TNNLS |
| PublicationTitleAlternate | IEEE Trans Neural Netw Learn Syst |
| PublicationYear | 2022 |
| Publisher | IEEE Institute of Electrical and Electronics Engineers (IEEE) The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher_xml | – name: IEEE – name: Institute of Electrical and Electronics Engineers (IEEE) – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| References | ref13 ref12 ref15 ref14 ref11 ref10 ref17 ref16 ref19 ref18 ref50 ref46 ref45 ref48 ref47 ref42 ref41 ref44 ref43 ref49 ref8 ref7 ref9 ref4 ref3 ref6 ref5 ref40 ref35 ref34 ref37 ref36 ref31 ref30 ref33 ref32 ref2 ref1 ref39 ref38 ref24 ref23 ref26 ref25 ref20 ref22 ref21 ref28 ref27 ref29 |
| References_xml | – ident: ref10 doi: 10.1109/TII.2018.2873175 – ident: ref1 doi: 10.1109/TIE.2016.2530789 – ident: ref18 doi: 10.1109/TSMC.2020.2999485 – ident: ref27 doi: 10.1016/j.automatica.2017.07.028 – ident: ref46 doi: 10.1007/s11432-019-2893-y – ident: ref6 doi: 10.1109/CCA.2013.6662773 – ident: ref38 doi: 10.1109/TSMC.2018.2882734 – ident: ref49 doi: 10.1109/TIE.2018.2884172 – ident: ref48 doi: 10.1109/TSMC.2019.2944900 – ident: ref45 doi: 10.1109/TCYB.2016.2581173 – ident: ref31 doi: 10.1109/TNNLS.2019.2952175 – ident: ref21 doi: 10.1109/TSMC.2018.2843523 – ident: ref35 doi: 10.1109/TIE.2017.2694391 – ident: ref47 doi: 10.1109/CDC.2005.1583507 – ident: ref39 doi: 10.1109/TMECH.2017.2721553 – ident: ref12 doi: 10.1016/j.automatica.2014.10.117 – ident: ref19 doi: 10.1109/TCST.2017.2780055 – ident: ref17 doi: 10.1109/TAC.2015.2471815 – ident: ref44 doi: 10.1016/j.automatica.2011.08.044 – ident: ref26 doi: 10.1109/TFUZZ.2015.2486811 – ident: ref40 doi: 10.1109/TCYB.2019.2923461 – ident: ref25 doi: 10.1109/ACCESS.2018.2805800 – ident: ref14 doi: 10.1109/TSMC.2016.2562506 – ident: ref4 doi: 10.1109/TMECH.2019.2951812 – ident: ref5 doi: 10.1109/TCST.2008.2012166 – ident: ref42 doi: 10.1109/TFUZZ.2018.2882173 – ident: ref8 doi: 10.1109/TFUZZ.2018.2826475 – ident: ref43 doi: 10.1007/s11432-019-2714-7 – ident: ref3 doi: 10.1109/TIE.2015.2432107 – ident: ref29 doi: 10.1109/TCYB.2017.2667680 – ident: ref41 doi: 10.1109/TFUZZ.2017.2761323 – ident: ref9 doi: 10.1109/TSMC.2015.2504552 – ident: ref36 doi: 10.1109/TNNLS.2017.2665581 – ident: ref50 doi: 10.1109/TCYB.2021.3055151 – ident: ref28 doi: 10.1109/TMECH.2020.2975567 – ident: ref16 doi: 10.1109/TIE.2019.2931230 – ident: ref32 doi: 10.1109/TIE.2019.2946554 – ident: ref37 doi: 10.1109/TNNLS.2018.2872595 – ident: ref13 doi: 10.1016/j.automatica.2014.09.006 – ident: ref20 doi: 10.1016/j.isatra.2016.04.006 – ident: ref24 doi: 10.1109/TCYB.2015.2456028 – ident: ref22 doi: 10.1016/j.automatica.2016.11.002 – ident: ref11 doi: 10.1109/TMECH.2020.2987963 – ident: ref34 doi: 10.1109/TNN.2010.2047115 – ident: ref30 doi: 10.1109/TCYB.2018.2808321 – ident: ref2 doi: 10.1007/BF00940460 – ident: ref15 doi: 10.1007/BF00934533 – ident: ref33 doi: 10.1109/TAC.2009.2020638 – ident: ref7 doi: 10.1109/TIE.2015.2421884 – ident: ref23 doi: 10.1109/TAC.2018.2874746 |
| SSID | ssj0000605649 ssib030786659 ssib058575017 ssib053393622 |
| Score | 2.6614454 |
| Snippet | This article focuses on the vibration reducing and angle tracking problems of a flexible unmanned spacecraft system subject to input nonlinearity, asymmetric... |
| SourceID | proquest pubmed crossref nii ieee |
| SourceType | Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 6226 |
| SubjectTerms | Adaptive control Adaptive neural network control Adaptive systems asymmetric constraint Asymmetry Backstepping backstepping technique Boundary control Control theory Feedback control Flexible spacecraft flexible unmanned spacecraft Liapunov functions Lyapunov methods Network control Neural networks Nonlinear systems Nonlinearity Parameter uncertainty Robustness (mathematics) Space vehicles Spacecraft Stability analysis Uncertainty Unmanned spacecraft Vibration vibration control Vibrations |
| Title | Adaptive Neural Network Control of a Flexible Spacecraft Subject to Input Nonlinearity and Asymmetric Output Constraint |
| URI | https://ieeexplore.ieee.org/document/9432801 https://cir.nii.ac.jp/crid/1872553967815468544 https://www.ncbi.nlm.nih.gov/pubmed/33999824 https://www.proquest.com/docview/2729636520 https://www.proquest.com/docview/2528814103 |
| Volume | 33 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV1Jb9UwELZKT1woUJZAi4zEDfLq2I4TH58qngqCcGgrvVtkx7ZU0SZVm6gqv54ZZ5FAgLhEkTJeohl75rNnIeQdqmSJET1c2ZDKXITU2kalXBqTeR2YHb0tKnVyLj9v8-0O-bDEwnjvo_OZX-FrvMt3XTPgUdmRloKXGKz1AIDbGKu1nKcwsMtVtHZ5pnjKRbGdY2SYPjqrqi-ngAZ5thKYK5th8T0BylmXXP6ikmKNFVA07cXF343OqHw2e-TrPO3R5-T7aujtqvnxW0bH__2vx-TRZIXS9Sg2T8iOb5-SvbnCA50W_D65WztzjRsixSQe0KIavcbp8ejhTrtADd1gUk176ekpAHAwQ03oKWxIeMJD-45-aqFXWo05OQwWy6OmdXR9e391hfW8Gvpt6JEEi4fGkhX9M3K--Xh2fJJOpRrSBjjdp7ALKFYEn-eOgdr1DFAIY00onDCBace4BSRstPMuAwQlmSktQLlCeW6dEUY8J7tt1_qXhPLARGgypYIAa4dJi_c3Tpfa5KYQNiQkm7lVN1Mec5zbZR3xDNN1ZHaNzK4nZifk_dLmeszi8U_qfeTRQjmxJyGHIBQwJj6zsgAwJjToejBCVZlLmZCDWVzqaSO4rTn0qITKOUvI2-UzLGG8lzGt7wagyXlZor-tSMiLUcyWsWcZffXnOb0mDznGY8TgyAOy298M_hCspN6-icvjJ1kxCEE |
| linkProvider | IEEE |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwzV3NbtQwEB6VcoALBQol0IKR4ISydezESQ4cVoVVly7h0K20t2AntlTRZldsVlV5Fl6Fd2MmfxIIuFXiEkWK4zjJ55lv7PkBeEUqOaSIHqGM88NIOt-YQvki1DqwqeOm9bbI1PFZ-GERLbbg-xALY61tnM_siE6bvfxyWWxoqewwDaVAidq5UJ7Y6ys00NZvp-_wb74WYvJ-fnTsdzUE_AKHUPsIT8VjZ6Oo5KgPLEd6zHnh4lJqx9OSC4Mmmk5LWwZI7UOuE4M2RqysMKWWWmK_t-A28oxItNFhwwoOdhyphl-LQAlfyHjRR-Xw9HCeZbNTtD9FMJKUnZtTuT-JdCBNRPiLEmyquqBqq87P_05zG3U32YEf_YdqvVy-jDa1GRXffssh-b9-yftwr-PZbNxOjAewZauHsNPXsGCdSNuFq3GpVyTyGaUpwTuy1i-eHbU-_GzpmGYTShtqLiw7XekCibZ2NUORS2tYrF6yaYW9sqzNOqKpHCDTVcnG6-vLS6pYVrBPm5qaUHnUpihH_QjObuT1H8N2tazsE2DCcemKQCknkc_x0NAOVZkmqY50LI3zIOjRkRddpnYa20XeWGw8zRtw5QSuvAOXB2-Ge1ZtnpJ_tt4lTAwtOzh4cIAgxGfSMUhiNDdlimwGabZKojD0YL-HZ96JunUusEclVSS4By-HyyikaOdJV3a5wTaRSBLyKJYe7LWwHp7dz4mnfx7TC7hzPP84y2fT7OQZ3BUUfdKEgu7Ddv11Yw-QE9bmeTM1GXy-aQT_BL_7Zck |
| 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=Adaptive+Neural+Network+Control+of+a+Flexible+Spacecraft+Subject+to+Input+Nonlinearity+and+Asymmetric+Output+Constraint&rft.jtitle=IEEE+transaction+on+neural+networks+and+learning+systems&rft.au=Liu%2C+Yu&rft.au=Chen%2C+Xiongbin&rft.au=Wu%2C+Yilin&rft.au=Cai%2C+He&rft.date=2022-11-01&rft.issn=2162-2388&rft.eissn=2162-2388&rft.volume=33&rft.issue=11&rft.spage=6226&rft_id=info:doi/10.1109%2FTNNLS.2021.3072907&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2162-237X&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2162-237X&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2162-237X&client=summon |