Fixed-Time Leader-Follower Consensus of Networked Nonlinear Systems via Event/Self-Triggered Control

This brief addresses the fixed-time event/self-triggered leader-follower consensus problems for networked multi-agent systems subject to nonlinear dynamics. First, we present an event-triggered control strategy to achieve the fixed-time consensus, and a new measurement error is designed to avoid Zen...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:IEEE transaction on neural networks and learning systems Ročník 31; číslo 11; s. 5029 - 5037
Hlavní autori: Liu, Jian, Zhang, Yanling, Yu, Yao, Sun, Changyin
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States IEEE 01.11.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
ISSN:2162-237X, 2162-2388, 2162-2388
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Abstract This brief addresses the fixed-time event/self-triggered leader-follower consensus problems for networked multi-agent systems subject to nonlinear dynamics. First, we present an event-triggered control strategy to achieve the fixed-time consensus, and a new measurement error is designed to avoid Zeno behavior. Then, two new self-triggered control strategies are presented to avoid continuous triggering condition monitoring. Moreover, under the proposed self-triggered control strategies, a strictly positive minimal triggering interval of each follower is given to exclude Zeno behavior. Compared with the existing fixed-time event-triggered results, we propose two new self-triggered control strategies, and the nonlinear term is more general. Finally, the performances of the consensus tracking algorithms are illustrated by a simulation example.
AbstractList This brief addresses the fixed-time event/self-triggered leader-follower consensus problems for networked multi-agent systems subject to nonlinear dynamics. First, we present an event-triggered control strategy to achieve the fixed-time consensus, and a new measurement error is designed to avoid Zeno behavior. Then, two new self-triggered control strategies are presented to avoid continuous triggering condition monitoring. Moreover, under the proposed self-triggered control strategies, a strictly positive minimal triggering interval of each follower is given to exclude Zeno behavior. Compared with the existing fixed-time event-triggered results, we propose two new self-triggered control strategies, and the nonlinear term is more general. Finally, the performances of the consensus tracking algorithms are illustrated by a simulation example.This brief addresses the fixed-time event/self-triggered leader-follower consensus problems for networked multi-agent systems subject to nonlinear dynamics. First, we present an event-triggered control strategy to achieve the fixed-time consensus, and a new measurement error is designed to avoid Zeno behavior. Then, two new self-triggered control strategies are presented to avoid continuous triggering condition monitoring. Moreover, under the proposed self-triggered control strategies, a strictly positive minimal triggering interval of each follower is given to exclude Zeno behavior. Compared with the existing fixed-time event-triggered results, we propose two new self-triggered control strategies, and the nonlinear term is more general. Finally, the performances of the consensus tracking algorithms are illustrated by a simulation example.
This brief addresses the fixed-time event/self-triggered leader-follower consensus problems for networked multi-agent systems subject to nonlinear dynamics. First, we present an event-triggered control strategy to achieve the fixed-time consensus, and a new measurement error is designed to avoid Zeno behavior. Then, two new self-triggered control strategies are presented to avoid continuous triggering condition monitoring. Moreover, under the proposed self-triggered control strategies, a strictly positive minimal triggering interval of each follower is given to exclude Zeno behavior. Compared with the existing fixed-time event-triggered results, we propose two new self-triggered control strategies, and the nonlinear term is more general. Finally, the performances of the consensus tracking algorithms are illustrated by a simulation example.
Author Sun, Changyin
Liu, Jian
Zhang, Yanling
Yu, Yao
Author_xml – sequence: 1
  givenname: Jian
  orcidid: 0000-0002-5622-5183
  surname: Liu
  fullname: Liu, Jian
  email: bkliujian@163.com
  organization: School of Automation, Southeast University, Nanjing, China
– sequence: 2
  givenname: Yanling
  surname: Zhang
  fullname: Zhang, Yanling
  email: yanlzhang@ustb.edu.cn
  organization: School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China
– sequence: 3
  givenname: Yao
  orcidid: 0000-0002-2619-2843
  surname: Yu
  fullname: Yu, Yao
  email: yuyao@ustb.edu.cn
  organization: School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China
– sequence: 4
  givenname: Changyin
  orcidid: 0000-0001-9269-334X
  surname: Sun
  fullname: Sun, Changyin
  email: cysun@seu.edu.cn
  organization: School of Automation, Southeast University, Nanjing, China
BackLink https://www.ncbi.nlm.nih.gov/pubmed/31905152$$D View this record in MEDLINE/PubMed
BookMark eNp9kU9vEzEQxS1UREvpFwAJrcSFy6b-v_YRRQ0gReGQIHGzvLuzlYvXLvZuS789Dgk59MBcZg6_N_M07zU6CzEAQm8JXhCC9fVus1lvFxQTvaBaNFjqF-iCEklrypQ6O83Nj3N0lfMdLiWxkFy_QueMaCyIoBeoX7nf0Nc7N0K1BttDqlfR-_gIqVrGkCHkOVdxqDYwPcb0E_pqE4N3AWyqtk95gjFXD85WNw8Qpust-KHeJXd7C6mgZcOUon-DXg7WZ7g69kv0fXWzW36p198-f11-WtcdE2SqhaYNU5ZzKVgjKVihO86x7iynVnJCBmlbYgXrZIt71RIColUwMFyIBhN2iT4e9t6n-GuGPJnR5Q68twHinA1ljFPOaIML-uEZehfnFIo7Q7lQjcAUs0K9P1JzO0Jv7pMbbXoy__5XAHoAuhRzTjCcEILNPifzNyezz8kccyoi9UzUuclObv8s6_z_pe8OUgcAp1tKF7uqYX8A2Iyerg
CODEN ITNNAL
CitedBy_id crossref_primary_10_3390_s22239537
crossref_primary_10_1109_TNNLS_2021_3057482
crossref_primary_10_1016_j_isatra_2022_03_016
crossref_primary_10_1016_j_neunet_2024_106808
crossref_primary_10_1109_TCSI_2021_3060789
crossref_primary_10_1109_TSMC_2025_3573704
crossref_primary_10_1109_TSMC_2021_3051156
crossref_primary_10_1016_j_neucom_2021_08_081
crossref_primary_10_1109_TSMC_2024_3490659
crossref_primary_10_1088_1742_6596_2891_10_102004
crossref_primary_10_1002_rnc_7285
crossref_primary_10_1038_s41598_022_12634_2
crossref_primary_10_1109_ACCESS_2023_3258547
crossref_primary_10_1109_TCSI_2021_3109454
crossref_primary_10_1016_j_jfranklin_2024_107241
crossref_primary_10_1016_j_ins_2024_120243
crossref_primary_10_1002_rnc_6358
crossref_primary_10_1049_cth2_12056
crossref_primary_10_1109_JAS_2021_1003916
crossref_primary_10_1109_TSMC_2023_3328001
crossref_primary_10_1007_s11063_021_10509_7
crossref_primary_10_1016_j_ast_2025_110741
crossref_primary_10_1007_s40747_021_00334_9
crossref_primary_10_1016_j_fraope_2023_100054
crossref_primary_10_1109_JAS_2022_105503
crossref_primary_10_1007_s00521_024_10918_4
crossref_primary_10_1016_j_jfranklin_2021_10_010
crossref_primary_10_1002_rnc_70094
crossref_primary_10_1007_s11432_024_4355_1
crossref_primary_10_1109_TCNS_2023_3280457
crossref_primary_10_1016_j_isatra_2025_06_002
crossref_primary_10_1109_TCSI_2022_3175713
crossref_primary_10_1109_TNNLS_2022_3166531
crossref_primary_10_1109_TSMC_2023_3341847
crossref_primary_10_1088_1674_1056_aceeeb
crossref_primary_10_1016_j_isatra_2023_03_010
crossref_primary_10_1016_j_neucom_2022_04_001
crossref_primary_10_1016_j_neucom_2024_128954
crossref_primary_10_1109_TCNS_2023_3274700
crossref_primary_10_1002_rnc_6182
crossref_primary_10_1016_j_cnsns_2023_107662
crossref_primary_10_1109_JSYST_2023_3262510
crossref_primary_10_1109_TCSI_2021_3071034
crossref_primary_10_1007_s12555_023_0633_y
crossref_primary_10_1080_00207179_2024_2446850
crossref_primary_10_1016_j_cnsns_2025_108828
crossref_primary_10_1109_TASE_2025_3532350
crossref_primary_10_1016_j_jfranklin_2023_07_013
crossref_primary_10_1016_j_neucom_2025_130324
crossref_primary_10_1007_s11768_020_0011_8
crossref_primary_10_1049_iet_cta_2020_0393
crossref_primary_10_1007_s12555_020_0792_z
crossref_primary_10_1016_j_neucom_2022_02_081
crossref_primary_10_1109_JAS_2023_123201
crossref_primary_10_1007_s11431_024_2715_5
crossref_primary_10_1007_s40815_021_01083_0
crossref_primary_10_1016_j_ins_2022_06_079
crossref_primary_10_1016_j_oceaneng_2022_113422
crossref_primary_10_1007_s11424_022_1036_5
crossref_primary_10_3390_sym14071368
crossref_primary_10_1109_TSMC_2023_3309867
crossref_primary_10_1007_s40747_024_01436_w
crossref_primary_10_1016_j_neucom_2021_03_126
crossref_primary_10_1016_j_neucom_2021_08_140
crossref_primary_10_1049_iet_cta_2019_1132
crossref_primary_10_1016_j_oceaneng_2023_116490
crossref_primary_10_1109_TCNS_2022_3233927
crossref_primary_10_1016_j_neunet_2023_06_032
crossref_primary_10_1016_j_amc_2024_128955
crossref_primary_10_1109_TSMC_2021_3063117
crossref_primary_10_1109_TCSI_2022_3164552
crossref_primary_10_1109_TCSII_2021_3128624
crossref_primary_10_1108_AA_10_2019_0178
crossref_primary_10_1109_TCSII_2021_3126857
crossref_primary_10_1109_TSMC_2021_3103013
crossref_primary_10_1155_2021_9028591
crossref_primary_10_1007_s40815_024_01786_0
crossref_primary_10_1016_j_neucom_2021_05_088
crossref_primary_10_1016_j_jfranklin_2021_09_023
crossref_primary_10_1016_j_neucom_2020_05_065
crossref_primary_10_1016_j_isatra_2022_03_005
crossref_primary_10_1109_TIV_2023_3237790
crossref_primary_10_1002_rnc_7691
crossref_primary_10_1109_TIV_2023_3306802
crossref_primary_10_1007_s11432_019_2815_7
crossref_primary_10_1016_j_isatra_2022_01_023
crossref_primary_10_1109_TSMC_2021_3051271
crossref_primary_10_1109_TASE_2025_3578418
crossref_primary_10_1016_j_sysconle_2023_105711
crossref_primary_10_1109_TCSII_2022_3217784
crossref_primary_10_1016_j_jfranklin_2022_12_010
crossref_primary_10_1109_TCYB_2021_3053627
crossref_primary_10_1007_s12555_022_0630_6
crossref_primary_10_1016_j_ins_2023_119471
crossref_primary_10_1109_JAS_2023_123444
crossref_primary_10_1080_00207179_2021_1916838
crossref_primary_10_1016_j_amc_2025_129630
crossref_primary_10_1109_ACCESS_2023_3262284
crossref_primary_10_1007_s11071_021_07152_1
crossref_primary_10_1109_TCYB_2020_3035358
crossref_primary_10_1002_asjc_3092
crossref_primary_10_1109_TCYB_2020_2999199
crossref_primary_10_1109_TSMC_2022_3146191
crossref_primary_10_1109_TASE_2023_3318832
crossref_primary_10_1109_JIOT_2025_3554098
crossref_primary_10_1007_s11071_025_11178_0
crossref_primary_10_1007_s12555_021_0144_7
crossref_primary_10_1007_s12555_021_0909_z
crossref_primary_10_1049_cth2_12136
crossref_primary_10_1109_TIE_2022_3176242
crossref_primary_10_1109_TNSE_2023_3335298
crossref_primary_10_1109_TSMC_2025_3560947
crossref_primary_10_1007_s11071_021_06612_y
crossref_primary_10_1109_TCYB_2020_3034013
crossref_primary_10_1371_journal_pone_0293424
crossref_primary_10_1016_j_jfranklin_2021_07_055
crossref_primary_10_1109_TNNLS_2022_3174416
crossref_primary_10_3390_app12157580
crossref_primary_10_1016_j_isatra_2021_10_006
crossref_primary_10_1016_j_cnsns_2022_106677
crossref_primary_10_1007_s11063_020_10374_w
crossref_primary_10_1631_FITEE_2000145
crossref_primary_10_1016_j_jfranklin_2022_05_041
crossref_primary_10_1109_TNNLS_2022_3153028
crossref_primary_10_1109_TSMC_2023_3272315
crossref_primary_10_1177_01423312221088087
crossref_primary_10_3390_math12223544
crossref_primary_10_1016_j_neucom_2022_06_038
crossref_primary_10_1109_TCSII_2022_3233794
crossref_primary_10_1016_j_jfranklin_2020_07_039
crossref_primary_10_1109_TCYB_2021_3125851
crossref_primary_10_1177_01423312241260916
crossref_primary_10_1016_j_amc_2024_128707
crossref_primary_10_1002_mma_9090
crossref_primary_10_1109_TSMC_2022_3221839
crossref_primary_10_1007_s11063_024_11482_7
crossref_primary_10_1109_TCSI_2024_3456555
crossref_primary_10_1093_imamci_dnaf010
crossref_primary_10_1109_TCYB_2020_3005964
crossref_primary_10_1007_s11063_021_10726_0
crossref_primary_10_1109_TFUZZ_2020_3046335
crossref_primary_10_1109_TFUZZ_2022_3169852
crossref_primary_10_1007_s12555_024_0397_z
crossref_primary_10_1109_TCYB_2022_3207325
crossref_primary_10_1109_TNNLS_2024_3424519
crossref_primary_10_1109_JAS_2020_1003315
crossref_primary_10_3390_jmse11020385
crossref_primary_10_1002_rnc_7546
crossref_primary_10_1007_s11063_021_10624_5
crossref_primary_10_1016_j_neunet_2023_07_046
crossref_primary_10_1007_s12555_022_0682_7
crossref_primary_10_1109_TCYB_2022_3160014
crossref_primary_10_1109_TCYB_2021_3054626
crossref_primary_10_1016_j_neucom_2024_129173
crossref_primary_10_1109_TCSII_2024_3371180
crossref_primary_10_1016_j_ins_2023_02_072
crossref_primary_10_1080_00207179_2023_2201638
crossref_primary_10_1109_TAC_2024_3466871
crossref_primary_10_1002_rnc_5900
crossref_primary_10_1109_TCSII_2021_3120791
crossref_primary_10_1109_JAS_2022_105809
crossref_primary_10_1109_TCSII_2020_2999480
crossref_primary_10_1109_ACCESS_2023_3308692
crossref_primary_10_1109_TNNLS_2021_3125145
crossref_primary_10_1109_ACCESS_2020_2987960
crossref_primary_10_1109_JAS_2021_1004359
crossref_primary_10_1016_j_jfranklin_2022_05_020
crossref_primary_10_1002_rnc_6280
crossref_primary_10_1109_TVT_2024_3432392
crossref_primary_10_1016_j_neucom_2025_129643
crossref_primary_10_1007_s12555_021_1076_y
crossref_primary_10_1007_s11063_020_10299_4
crossref_primary_10_1007_s11071_024_10116_w
crossref_primary_10_1016_j_ins_2023_119397
crossref_primary_10_1109_JAS_2023_123405
crossref_primary_10_1016_j_chaos_2024_115649
crossref_primary_10_1016_j_isatra_2021_03_031
crossref_primary_10_1016_j_neucom_2022_07_052
crossref_primary_10_1109_JAS_2022_105413
Cites_doi 10.1016/j.automatica.2006.06.015
10.1109/TNNLS.2018.2817880
10.1109/TSMC.2018.2853809
10.1109/TAC.2017.2729502
10.1109/TNNLS.2018.2873676
10.1080/00207179.2018.1436193
10.1007/s11071-017-3945-8
10.1109/TCSI.2017.2777504
10.1109/TNNLS.2018.2878463
10.1002/rnc.4098
10.1109/TCYB.2015.2398892
10.1109/TAC.2018.2852605
10.1109/TAC.2011.2179869
10.1109/TAC.2014.2365073
10.1109/TAC.2017.2693824
10.1109/TCYB.2018.2794759
10.1016/j.automatica.2019.01.026
10.1109/TAC.2010.2041610
10.1049/iet-cta.2014.0219
10.1016/j.automatica.2015.02.001
10.1016/j.sysconle.2016.03.006
10.1109/TSMC.2016.2623634
10.1109/TSMC.2018.2876334
10.1016/j.jfranklin.2017.12.026
10.1016/j.automatica.2013.07.024
10.1109/TAC.2005.846556
10.1109/TAC.2011.2174666
10.1109/TCYB.2015.2496977
10.1109/TCYB.2015.2510746
10.1016/j.automatica.2015.01.021
10.1080/00207721.2018.1460411
10.1049/iet-cta.2014.1301
10.1016/j.automatica.2011.02.045
10.1080/00207721.2014.925608
10.1016/j.automatica.2009.07.012
10.1016/j.neucom.2017.05.007
10.1109/TCYB.2018.2857400
10.1109/TAC.2004.834113
10.1002/rnc.1814
10.1016/j.automatica.2015.02.033
10.1049/iet-cta.2017.0085
10.1016/j.ins.2015.12.031
10.1109/TNNLS.2017.2651402
10.1109/TAC.2014.2351431
10.1109/TCST.2017.2757448
10.1016/j.ins.2018.12.037
10.1109/TAC.2011.2146830
10.1109/TCYB.2017.2788874
10.1007/978-94-015-7793-9
10.1049/iet-cta.2014.0295
10.1109/TAC.2018.2857723
10.1016/j.automatica.2013.11.023
10.1109/TCSII.2015.2482158
10.1109/TNNLS.2018.2868986
10.1016/j.automatica.2017.09.017
10.1109/TNNLS.2016.2519894
10.1109/TIE.2017.2701775
10.1016/j.neunet.2015.11.002
10.1109/CDC.2009.5399776
ContentType Journal Article
Copyright Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2020
Copyright_xml – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2020
DBID 97E
RIA
RIE
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.2019.2957069
DatabaseName IEEE All-Society Periodicals Package (ASPP) 2005–Present
IEEE All-Society Periodicals Package (ASPP) 1998–Present
IEEE Electronic Library (IEL)
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 MEDLINE - Academic
PubMed

Materials Research Database
Database_xml – sequence: 1
  dbid: NPM
  name: PubMed
  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://ieeexplore.ieee.org/
  sourceTypes: Publisher
– sequence: 3
  dbid: 7X8
  name: MEDLINE - Academic
  url: https://search.proquest.com/medline
  sourceTypes: Aggregation Database
DeliveryMethod fulltext_linktorsrc
Discipline Computer Science
EISSN 2162-2388
EndPage 5037
ExternalDocumentID 31905152
10_1109_TNNLS_2019_2957069
8950287
Genre orig-research
Journal Article
GrantInformation_xml – fundername: National Natural Science Foundation of China
  grantid: 61921004; 61603036; 61703037; U1713209; 61520106009
  funderid: 10.13039/501100001809
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
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
ID FETCH-LOGICAL-c351t-592738a44653762ea59c4409ca42a6411f6ab1a53c6b0d8b11e5b8ef30ca47013
IEDL.DBID RIE
ISICitedReferencesCount 229
ISICitedReferencesURI http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000587699700051&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN 2162-237X
2162-2388
IngestDate Thu Oct 02 11:57:45 EDT 2025
Mon Jun 30 04:42:17 EDT 2025
Thu Jan 02 23:00:43 EST 2025
Tue Nov 18 21:24:08 EST 2025
Sat Nov 29 01:40:05 EST 2025
Wed Aug 27 02:29:45 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-c351t-592738a44653762ea59c4409ca42a6411f6ab1a53c6b0d8b11e5b8ef30ca47013
Notes ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ORCID 0000-0002-2619-2843
0000-0001-9269-334X
0000-0002-5622-5183
PMID 31905152
PQID 2458750203
PQPubID 85436
PageCount 9
ParticipantIDs proquest_miscellaneous_2334243270
proquest_journals_2458750203
crossref_primary_10_1109_TNNLS_2019_2957069
ieee_primary_8950287
pubmed_primary_31905152
crossref_citationtrail_10_1109_TNNLS_2019_2957069
PublicationCentury 2000
PublicationDate 2020-11-01
PublicationDateYYYYMMDD 2020-11-01
PublicationDate_xml – month: 11
  year: 2020
  text: 2020-11-01
  day: 01
PublicationDecade 2020
PublicationPlace United States
PublicationPlace_xml – name: United States
– name: Piscataway
PublicationTitle IEEE transaction on neural networks and learning systems
PublicationTitleAbbrev TNNLS
PublicationTitleAlternate IEEE Trans Neural Netw Learn Syst
PublicationYear 2020
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 ref57
ref13
ref56
ref12
ref15
ref58
ref14
ref53
ref52
ref55
ref11
ref54
ref10
ref17
ref16
ref19
ref18
filippov (ref59) 1988
ref51
ref50
ref46
ref45
ref48
ref47
ref42
ref41
ref44
ref43
ref49
cao (ref8) 2012; 57
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: ref16
  doi: 10.1016/j.automatica.2006.06.015
– ident: ref5
  doi: 10.1109/TNNLS.2018.2817880
– ident: ref39
  doi: 10.1109/TSMC.2018.2853809
– ident: ref30
  doi: 10.1109/TAC.2017.2729502
– ident: ref21
  doi: 10.1109/TNNLS.2018.2873676
– ident: ref49
  doi: 10.1080/00207179.2018.1436193
– ident: ref28
  doi: 10.1007/s11071-017-3945-8
– ident: ref40
  doi: 10.1109/TCSI.2017.2777504
– ident: ref12
  doi: 10.1109/TNNLS.2018.2878463
– ident: ref54
  doi: 10.1002/rnc.4098
– ident: ref37
  doi: 10.1109/TCYB.2015.2398892
– ident: ref29
  doi: 10.1109/TAC.2018.2852605
– ident: ref13
  doi: 10.1109/TAC.2011.2179869
– ident: ref42
  doi: 10.1109/TAC.2014.2365073
– ident: ref45
  doi: 10.1109/TAC.2017.2693824
– ident: ref26
  doi: 10.1109/TCYB.2018.2794759
– ident: ref53
  doi: 10.1016/j.automatica.2019.01.026
– ident: ref17
  doi: 10.1109/TAC.2010.2041610
– ident: ref36
  doi: 10.1049/iet-cta.2014.0219
– ident: ref20
  doi: 10.1016/j.automatica.2015.02.001
– ident: ref25
  doi: 10.1016/j.sysconle.2016.03.006
– ident: ref22
  doi: 10.1109/TSMC.2016.2623634
– ident: ref57
  doi: 10.1109/TSMC.2018.2876334
– ident: ref52
  doi: 10.1016/j.jfranklin.2017.12.026
– ident: ref14
  doi: 10.1016/j.automatica.2013.07.024
– ident: ref2
  doi: 10.1109/TAC.2005.846556
– ident: ref35
  doi: 10.1109/TAC.2011.2174666
– ident: ref44
  doi: 10.1109/TCYB.2015.2496977
– ident: ref43
  doi: 10.1109/TCYB.2015.2510746
– ident: ref24
  doi: 10.1016/j.automatica.2015.01.021
– ident: ref6
  doi: 10.1080/00207721.2018.1460411
– ident: ref23
  doi: 10.1049/iet-cta.2014.1301
– ident: ref18
  doi: 10.1016/j.automatica.2011.02.045
– ident: ref58
  doi: 10.1080/00207721.2014.925608
– ident: ref3
  doi: 10.1016/j.automatica.2009.07.012
– ident: ref47
  doi: 10.1016/j.neucom.2017.05.007
– ident: ref4
  doi: 10.1109/TCYB.2018.2857400
– ident: ref1
  doi: 10.1109/TAC.2004.834113
– ident: ref10
  doi: 10.1002/rnc.1814
– ident: ref9
  doi: 10.1016/j.automatica.2015.02.033
– ident: ref55
  doi: 10.1049/iet-cta.2017.0085
– ident: ref50
  doi: 10.1016/j.ins.2015.12.031
– ident: ref7
  doi: 10.1109/TNNLS.2017.2651402
– ident: ref19
  doi: 10.1109/TAC.2014.2351431
– ident: ref51
  doi: 10.1109/TCST.2017.2757448
– ident: ref56
  doi: 10.1016/j.ins.2018.12.037
– volume: 57
  start-page: 33
  year: 2012
  ident: ref8
  article-title: Distributed coordinated tracking with reduced interaction via a variable structure approach
  publication-title: IEEE Trans Autom Control
  doi: 10.1109/TAC.2011.2146830
– ident: ref32
  doi: 10.1109/TCYB.2017.2788874
– year: 1988
  ident: ref59
  publication-title: Differential Equations with Discontinuous Righthand Sides
  doi: 10.1007/978-94-015-7793-9
– ident: ref11
  doi: 10.1049/iet-cta.2014.0295
– ident: ref48
  doi: 10.1109/TAC.2018.2857723
– ident: ref41
  doi: 10.1016/j.automatica.2013.11.023
– ident: ref38
  doi: 10.1109/TCSII.2015.2482158
– ident: ref46
  doi: 10.1109/TNNLS.2018.2868986
– ident: ref31
  doi: 10.1016/j.automatica.2017.09.017
– ident: ref15
  doi: 10.1109/TNNLS.2016.2519894
– ident: ref27
  doi: 10.1109/TIE.2017.2701775
– ident: ref33
  doi: 10.1016/j.neunet.2015.11.002
– ident: ref34
  doi: 10.1109/CDC.2009.5399776
SSID ssj0000605649
Score 2.6840868
Snippet This brief addresses the fixed-time event/self-triggered leader-follower consensus problems for networked multi-agent systems subject to nonlinear dynamics....
This brief addresses the fixed-time event/self-triggered leader–follower consensus problems for networked multi-agent systems subject to nonlinear dynamics....
SourceID proquest
pubmed
crossref
ieee
SourceType Aggregation Database
Index Database
Enrichment Source
Publisher
StartPage 5029
SubjectTerms Algorithms
Condition monitoring
Consensus algorithm
Dynamical systems
Error analysis
Fixed-time consensus tracking
Heuristic algorithms
Learning systems
Measurement errors
multi-agent system (MAS)
Multiagent systems
Nonlinear control
Nonlinear dynamics
Nonlinear systems
self-triggered control
Topology
Title Fixed-Time Leader-Follower Consensus of Networked Nonlinear Systems via Event/Self-Triggered Control
URI https://ieeexplore.ieee.org/document/8950287
https://www.ncbi.nlm.nih.gov/pubmed/31905152
https://www.proquest.com/docview/2458750203
https://www.proquest.com/docview/2334243270
Volume 31
WOSCitedRecordID wos000587699700051&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: 2162-2388
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0000605649
  issn: 2162-237X
  databaseCode: RIE
  dateStart: 20120101
  isFulltext: true
  titleUrlDefault: https://ieeexplore.ieee.org/
  providerName: IEEE
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3da9RAEB_a4oMvVq0faWtZwTfdXnY3yWYfRXr4IEHoCfcW9ityUO6kd1f8853ZzQUfVPAtkMkmZGZ2ZnY-fgDvBonaHCwqkg-So4QEbts2cnRdRUCLOASTwSZ017XLpfl6BB-mXpgYYyo-i9d0mXL5YeP3dFQ2a02N5lAfw7HWOvdqTecpJfrlTfJ2pWgkl0ovDz0ypZktuu7LLRVymWtpal1SffNvdigBq_zdx0y2Zn76f1_5FJ6MPiX7mIXgGRzF9XM4PeA1sFF9zyDMVz9j4NT1wTK0Jp-jGBBOGiPgTkK92LLNwLpcGx4D6_IkDYur5NHm7GFl2Q0VSc5u493AFxjdfye8T1qBit5fwLf5zeLTZz6iLHCvarHjtaHuHJsGreHOGG1tfIVRn7eVtE0lxNBYJ2ytfOPK0DohYu3aOKgSKTR6kC_hZL1Zx9fA5NA65V3pyDGJjTWtQkloqui8st7FAsThn_d-HEFOSBh3fQpFStMnPvXEp37kUwHvp2d-5AEc_6Q-I4ZMlCMvCrg8sLYfdXTby6rGYI0ysQW8nW6jdlHKxK7jZo80SlWyUlKXBbzKIjGtjZsXAeTI8z-_8wIeS4rNU9_iJZzs7vfxDTzyD7vV9v4KRXjZXiUR_gVhBuz2
linkProvider IEEE
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3di9QwEB_OU_BePPXUq54awTfNbZM0bfMocsuJaxFuhX0raZLKwrErt7uHf74zSbf4oIJvhU7T0pnJzGQ-fgBve4na7C0qkvOSo4R4bus6cHRdhUeL2HuTwCaqpqkXC_P1AN6PvTAhhFh8Fs7pMuby_drt6KhsUhuN5rC6A3d1UUiRurXGE5UcPfMy-rtSlJJLVS32XTK5mcybZnZFpVzmXBpd5VTh_JslitAqf_cyo7WZHv_fdz6EB4NXyT4kMXgEB2H1GI73iA1sUOAT8NPlz-A59X2wBK7JpygIhJTGCLqTcC82bN2zJlWHB8-aNEvD4ippuDm7XVp2QWWSk6tw3fM5xvffCfGTVqCy9yfwbXox_3jJB5wF7pQWW64N9efYOGoN98ZgtXEFxn3OFtKWhRB9aTthtXJll_u6EyLorg69ypGiQh_yKRyu1qtwCkz2dadcl3fkmoTSmlqhLJRF6JyyrgsZiP0_b90whJywMK7bGIzkpo18aolP7cCnDN6Nz_xIIzj-SX1CDBkpB15kcLZnbTto6aaVhcZwjXKxGbwZb6N-UdLErsJ6hzRKFbJQssozeJZEYlwbty-CyJHP__zO13D_cv5l1s4-NZ9fwJGkSD12MZ7B4fZmF17CPXe7XW5uXkVB_gVCQ-9V
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=Fixed-Time+Leader-Follower+Consensus+of+Networked+Nonlinear+Systems+via+Event%2FSelf-Triggered+Control&rft.jtitle=IEEE+transaction+on+neural+networks+and+learning+systems&rft.au=Liu%2C+Jian&rft.au=Zhang%2C+Yanling&rft.au=Yu%2C+Yao&rft.au=Sun%2C+Changyin&rft.date=2020-11-01&rft.eissn=2162-2388&rft_id=info:doi/10.1109%2FTNNLS.2019.2957069&rft_id=info%3Apmid%2F31905152&rft.externalDocID=31905152
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