Reduced-Order Observer-Based Dynamic Event-Triggered Adaptive NN Control for Stochastic Nonlinear Systems Subject to Unknown Input Saturation

In this article, a dynamic event-triggered control scheme for a class of stochastic nonlinear systems with unknown input saturation and partially unmeasured states is presented. First, a dynamic event-triggered mechanism (DEM) is designed to reduce some unnecessary transmissions from controller to a...

Full description

Saved in:
Bibliographic Details
Published inIEEE transaction on neural networks and learning systems Vol. 32; no. 4; pp. 1678 - 1690
Main Authors Wang, Lijie, Chen, C. L. Philip
Format Journal Article
LanguageEnglish
Published United States IEEE 01.04.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Online AccessGet full text

Cover

Loading…
Abstract In this article, a dynamic event-triggered control scheme for a class of stochastic nonlinear systems with unknown input saturation and partially unmeasured states is presented. First, a dynamic event-triggered mechanism (DEM) is designed to reduce some unnecessary transmissions from controller to actuator so as to achieve better resource efficiency. Unlike most existing event-triggered mechanisms, in which the threshold parameters are always fixed, the threshold parameter in the developed event-triggered condition is dynamically adjusted according to a dynamic rule. Second, an improved neural network that considers the reconstructed error is introduced to approximate the unknown nonlinear terms existed in the considered systems. Third, an auxiliary system with the same order as the considered system is constructed to deal with the influence of asymmetric input saturation, which is distinct from most existing methods for nonlinear systems with input saturation. Assuming that the partial state is unavailable in the system, a reduced-order observer is presented to estimate them. Furthermore, it is theoretically proven that the obtained control scheme can achieve the desired objects. Finally, a one-link manipulator system and a three-degree-of-freedom ship maneuvering system are presented to illustrate the effectiveness of the proposed control method.
AbstractList In this article, a dynamic event-triggered control scheme for a class of stochastic nonlinear systems with unknown input saturation and partially unmeasured states is presented. First, a dynamic event-triggered mechanism (DEM) is designed to reduce some unnecessary transmissions from controller to actuator so as to achieve better resource efficiency. Unlike most existing event-triggered mechanisms, in which the threshold parameters are always fixed, the threshold parameter in the developed event-triggered condition is dynamically adjusted according to a dynamic rule. Second, an improved neural network that considers the reconstructed error is introduced to approximate the unknown nonlinear terms existed in the considered systems. Third, an auxiliary system with the same order as the considered system is constructed to deal with the influence of asymmetric input saturation, which is distinct from most existing methods for nonlinear systems with input saturation. Assuming that the partial state is unavailable in the system, a reduced-order observer is presented to estimate them. Furthermore, it is theoretically proven that the obtained control scheme can achieve the desired objects. Finally, a one-link manipulator system and a three-degree-of-freedom ship maneuvering system are presented to illustrate the effectiveness of the proposed control method.In this article, a dynamic event-triggered control scheme for a class of stochastic nonlinear systems with unknown input saturation and partially unmeasured states is presented. First, a dynamic event-triggered mechanism (DEM) is designed to reduce some unnecessary transmissions from controller to actuator so as to achieve better resource efficiency. Unlike most existing event-triggered mechanisms, in which the threshold parameters are always fixed, the threshold parameter in the developed event-triggered condition is dynamically adjusted according to a dynamic rule. Second, an improved neural network that considers the reconstructed error is introduced to approximate the unknown nonlinear terms existed in the considered systems. Third, an auxiliary system with the same order as the considered system is constructed to deal with the influence of asymmetric input saturation, which is distinct from most existing methods for nonlinear systems with input saturation. Assuming that the partial state is unavailable in the system, a reduced-order observer is presented to estimate them. Furthermore, it is theoretically proven that the obtained control scheme can achieve the desired objects. Finally, a one-link manipulator system and a three-degree-of-freedom ship maneuvering system are presented to illustrate the effectiveness of the proposed control method.
In this article, a dynamic event-triggered control scheme for a class of stochastic nonlinear systems with unknown input saturation and partially unmeasured states is presented. First, a dynamic event-triggered mechanism (DEM) is designed to reduce some unnecessary transmissions from controller to actuator so as to achieve better resource efficiency. Unlike most existing event-triggered mechanisms, in which the threshold parameters are always fixed, the threshold parameter in the developed event-triggered condition is dynamically adjusted according to a dynamic rule. Second, an improved neural network that considers the reconstructed error is introduced to approximate the unknown nonlinear terms existed in the considered systems. Third, an auxiliary system with the same order as the considered system is constructed to deal with the influence of asymmetric input saturation, which is distinct from most existing methods for nonlinear systems with input saturation. Assuming that the partial state is unavailable in the system, a reduced-order observer is presented to estimate them. Furthermore, it is theoretically proven that the obtained control scheme can achieve the desired objects. Finally, a one-link manipulator system and a three-degree-of-freedom ship maneuvering system are presented to illustrate the effectiveness of the proposed control method.
Author Wang, Lijie
Chen, C. L. Philip
Author_xml – sequence: 1
  givenname: Lijie
  orcidid: 0000-0002-9961-5843
  surname: Wang
  fullname: Wang, Lijie
  email: lijiewang1@gmail.com
  organization: Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Macau
– sequence: 2
  givenname: C. L. Philip
  orcidid: 0000-0001-5451-7230
  surname: Chen
  fullname: Chen, C. L. Philip
  email: philip.chen@ieee.org
  organization: School of Computer Science and Engineering, South China University of Technology, Guangzhou, China
BackLink https://www.ncbi.nlm.nih.gov/pubmed/32452775$$D View this record in MEDLINE/PubMed
BookMark eNp9kctuEzEUhkeoiJbSFwAJWWLDZoIv47ksSyhQKZpIJJXYWbbnTHGYsVPbE5SH4J1xm7SLLvDGR0ffd3Ts_3V2Yp2FLHtL8IwQ3Hxat-1iNaOY4hlt6pLW5EV2RklJc8rq-uSprn6eZhchbHA6JeZl0bzKThktOK0qfpb9_QHdpKHLl74Dj5YqgN-Bzz_LAB36srdyNBpd7cDGfO3N7S341L_s5DaaHaC2RXNno3cD6p1Hq-j0LxliUlpnB2NBpuY-RBgDWk1qAzqi6NCN_W3dH4uu7XaKaCXj5GU0zr7JXvZyCHBxvM-zm69X6_n3fLH8dj2_XOSacRLzpiqwYjVTJVa8plj3tFO9IrzQVPKyV5WscckoxqkEqZlmUPCKKql7Tphi59nHw9ytd3cThChGEzQMg7TgpiBogcsmGU2d0A_P0I2bvE3bCcpxQxtc0CpR74_UpEboxNabUfq9ePzoBNQHQHsXgodeaBMf3hy9NIMgWNzHKh5iFfeximOsSaXP1Mfp_5XeHSQDAE9Cg5uSE8z-AVNArtM
CODEN ITNNAL
CitedBy_id crossref_primary_10_1007_s12555_022_0504_y
crossref_primary_10_1109_TNNLS_2021_3123637
crossref_primary_10_1002_asjc_2640
crossref_primary_10_1007_s40815_023_01540_y
crossref_primary_10_1109_TNNLS_2022_3203419
crossref_primary_10_1016_j_ins_2022_04_048
crossref_primary_10_1007_s12555_021_0921_3
crossref_primary_10_1109_TFUZZ_2024_3392632
crossref_primary_10_1109_TCSII_2022_3150351
crossref_primary_10_1016_j_jfranklin_2024_107241
crossref_primary_10_1016_j_isatra_2023_11_022
crossref_primary_10_1109_JSYST_2021_3137363
crossref_primary_10_1016_j_jfranklin_2022_12_026
crossref_primary_10_1109_ACCESS_2024_3365542
crossref_primary_10_1016_j_jfranklin_2024_107242
crossref_primary_10_1109_TFUZZ_2022_3228012
crossref_primary_10_1002_acs_3812
crossref_primary_10_1177_09596518241233318
crossref_primary_10_1016_j_jfranklin_2022_06_031
crossref_primary_10_1016_j_isatra_2025_01_024
crossref_primary_10_1109_TFUZZ_2022_3189412
crossref_primary_10_1109_TNNLS_2023_3265637
crossref_primary_10_1016_j_actaastro_2022_07_035
crossref_primary_10_1016_j_neunet_2023_06_023
crossref_primary_10_1016_j_oceaneng_2024_119161
crossref_primary_10_1177_09596518241263894
crossref_primary_10_1016_j_isatra_2022_01_005
crossref_primary_10_1007_s12555_022_0134_4
crossref_primary_10_1002_rnc_7799
crossref_primary_10_1016_j_isatra_2024_11_015
crossref_primary_10_1007_s11063_021_10575_x
crossref_primary_10_1002_acs_3705
crossref_primary_10_1016_j_oceaneng_2022_113147
crossref_primary_10_1007_s11071_024_09355_8
crossref_primary_10_1007_s11071_024_10269_8
crossref_primary_10_1109_ACCESS_2023_3345250
crossref_primary_10_1109_JAS_2020_1003596
crossref_primary_10_1109_TNNLS_2022_3201695
crossref_primary_10_1007_s11071_022_07513_4
crossref_primary_10_1007_s11431_022_2126_7
crossref_primary_10_1109_TNNLS_2022_3203074
crossref_primary_10_1016_j_amc_2022_126973
crossref_primary_10_1016_j_fss_2024_109180
crossref_primary_10_1016_j_ejcon_2024_101091
crossref_primary_10_1002_rnc_7584
crossref_primary_10_1002_asjc_2902
crossref_primary_10_1016_j_oceaneng_2023_114524
crossref_primary_10_1007_s11071_024_09578_9
crossref_primary_10_1051_sands_2023024
crossref_primary_10_1155_2020_3959806
crossref_primary_10_3390_jmse10091203
crossref_primary_10_1109_JSYST_2022_3211207
crossref_primary_10_3934_mbe_2023335
crossref_primary_10_1016_j_isatra_2022_03_011
crossref_primary_10_1080_00207721_2024_2395930
crossref_primary_10_1109_TIE_2024_3379644
crossref_primary_10_1109_TCYB_2020_3030028
crossref_primary_10_3390_jmse10020227
crossref_primary_10_1016_j_amc_2024_128725
crossref_primary_10_1109_TFUZZ_2023_3281780
crossref_primary_10_1109_TITS_2024_3463181
crossref_primary_10_1109_TITS_2023_3326271
crossref_primary_10_1002_acs_3325
crossref_primary_10_1016_j_chaos_2023_113777
crossref_primary_10_1109_MSMC_2024_3358065
crossref_primary_10_1007_s12555_021_0543_9
crossref_primary_10_1016_j_oceaneng_2022_113240
crossref_primary_10_1109_TII_2021_3080841
crossref_primary_10_1007_s11071_024_09319_y
crossref_primary_10_1007_s12555_022_0501_1
crossref_primary_10_1109_TFUZZ_2022_3175606
crossref_primary_10_1007_s11071_024_09751_0
crossref_primary_10_1109_TFUZZ_2021_3052095
crossref_primary_10_1016_j_cnsns_2022_107070
crossref_primary_10_1109_TCSII_2021_3102331
crossref_primary_10_1109_TCYB_2022_3205765
crossref_primary_10_1016_j_eswa_2025_127179
crossref_primary_10_1109_LRA_2024_3405386
crossref_primary_10_1109_TNNLS_2021_3118089
crossref_primary_10_1016_j_oceaneng_2022_112144
crossref_primary_10_1109_ACCESS_2021_3119611
crossref_primary_10_1002_acs_3977
crossref_primary_10_1109_TCSII_2022_3200053
crossref_primary_10_1002_asjc_2551
crossref_primary_10_1007_s11071_022_07454_y
crossref_primary_10_1109_TNNLS_2023_3292154
crossref_primary_10_1109_TNNLS_2021_3140106
crossref_primary_10_1109_TCSI_2022_3232915
crossref_primary_10_1109_TNNLS_2021_3105681
crossref_primary_10_1109_TCYB_2021_3071336
crossref_primary_10_1109_TNNLS_2022_3210269
crossref_primary_10_1016_j_sysconle_2024_105871
crossref_primary_10_1016_j_adhoc_2023_103278
crossref_primary_10_1109_JETCAS_2022_3230416
crossref_primary_10_1109_TCYB_2024_3446795
crossref_primary_10_1109_TVT_2023_3338518
crossref_primary_10_1002_rnc_6269
crossref_primary_10_1007_s40815_022_01429_2
crossref_primary_10_1002_rnc_7631
crossref_primary_10_1002_rnc_6386
crossref_primary_10_1109_TCYB_2020_3044883
crossref_primary_10_1016_j_asr_2023_09_024
crossref_primary_10_1109_TCYB_2022_3219098
crossref_primary_10_1155_2021_9948044
crossref_primary_10_1007_s11071_022_07655_5
crossref_primary_10_1109_ACCESS_2022_3224722
crossref_primary_10_1108_EC_12_2022_0748
crossref_primary_10_1109_TCYB_2022_3226873
crossref_primary_10_1002_acs_3863
crossref_primary_10_1109_TCYB_2021_3091580
crossref_primary_10_1109_TITS_2023_3270723
crossref_primary_10_1177_01423312231174944
crossref_primary_10_1109_TCYB_2021_3128231
crossref_primary_10_1109_TAI_2023_3247550
crossref_primary_10_1016_j_ins_2023_119420
crossref_primary_10_1109_ACCESS_2025_3529130
crossref_primary_10_1109_TSMC_2021_3089944
crossref_primary_10_1109_TSMC_2024_3444007
crossref_primary_10_3390_math12040549
crossref_primary_10_1109_TSG_2024_3370912
crossref_primary_10_1109_TCSI_2022_3205335
crossref_primary_10_1109_TCYB_2022_3164977
crossref_primary_10_1109_TVT_2022_3184305
crossref_primary_10_1016_j_fss_2022_03_005
crossref_primary_10_1016_j_oceaneng_2023_116066
crossref_primary_10_1109_JSYST_2021_3079460
crossref_primary_10_1109_TNNLS_2022_3208611
crossref_primary_10_1109_TNNLS_2021_3105664
crossref_primary_10_1080_00207721_2022_2053892
crossref_primary_10_1016_j_amc_2022_126942
crossref_primary_10_1109_TSMC_2022_3185648
crossref_primary_10_1109_TIV_2023_3317336
crossref_primary_10_1177_01423312221090734
Cites_doi 10.1109/TNNLS.2017.2732240
10.1016/S0167-6911(97)00068-6
10.1109/TFUZZ.2017.2686373
10.1109/TNNLS.2018.2877195
10.1109/TNNLS.2017.2760903
10.1109/TNNLS.2016.2614002
10.1109/TAC.2016.2600340
10.1016/j.automatica.2019.02.024
10.1109/TCNS.2015.2428531
10.1109/TAC.2016.2594204
10.1109/TAC.2005.863501
10.1109/TFUZZ.2017.2774185
10.1109/TFUZZ.2018.2882173
10.1109/TNNLS.2015.2420661
10.1016/j.automatica.2011.01.025
10.1109/9.486648
10.1109/TSMC.2017.2719629
10.1109/TIE.2011.2107719
10.1109/TCYB.2016.2594046
10.1109/TNNLS.2015.2490168
10.1109/TNN.2005.849824
10.1109/TNNLS.2015.2412121
10.1109/TSMC.2016.2557222
10.1109/TNNLS.2018.2869375
10.1109/TCYB.2017.2707178
10.1109/TNNLS.2014.2305443
10.1109/TIE.2013.2290757
10.1109/TIE.2016.2597763
10.1109/TFUZZ.2017.2750619
10.1109/TAC.2007.904277
10.1109/TFUZZ.2016.2633325
10.1109/TSMC.2018.2883907
10.1109/TNN.2011.2159865
10.1016/j.fss.2017.05.010
10.1109/TAC.2014.2363603
10.1109/72.822511
10.1109/TSMC.2016.2531680
10.1109/TNNLS.2016.2538779
10.1109/TCYB.2018.2825984
10.1109/TSMC.2015.2429555
10.1109/TIE.2017.2701778
10.1016/j.automatica.2019.05.033
ContentType Journal Article
Copyright Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2021
Copyright_xml – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2021
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.2020.2986281
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

Materials Research Database
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 1690
ExternalDocumentID 32452775
10_1109_TNNLS_2020_2986281
9096510
Genre orig-research
Journal Article
GrantInformation_xml – fundername: National Key Research and Development Program of China
  grantid: 2019YFB1703600
  funderid: 10.13039/501100012166
– fundername: Science and Technology Development Fund, Macau
  grantid: 079/2017/A2; 0119/2018/A3
– fundername: Multiyear Research Grants of the University of Macau
– fundername: National Natural Science Foundation of China
  grantid: 61751202; 61751205; U1813203; U1801262
  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
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-c351t-9740b383b60b5820cf2dbfb154c2a56fb7a8063200fb7eac3c3e4572bacf513b3
IEDL.DBID RIE
ISSN 2162-237X
2162-2388
IngestDate Fri Jul 11 05:54:43 EDT 2025
Mon Jun 30 02:30:41 EDT 2025
Thu Apr 03 07:07:19 EDT 2025
Tue Jul 01 00:27:34 EDT 2025
Thu Apr 24 22:50:51 EDT 2025
Wed Aug 27 02:44:53 EDT 2025
IsPeerReviewed false
IsScholarly true
Issue 4
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-9740b383b60b5820cf2dbfb154c2a56fb7a8063200fb7eac3c3e4572bacf513b3
Notes ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ORCID 0000-0001-5451-7230
0000-0002-9961-5843
PMID 32452775
PQID 2509290427
PQPubID 85436
PageCount 13
ParticipantIDs proquest_journals_2509290427
pubmed_primary_32452775
crossref_citationtrail_10_1109_TNNLS_2020_2986281
crossref_primary_10_1109_TNNLS_2020_2986281
proquest_miscellaneous_2406945798
ieee_primary_9096510
ProviderPackageCode CITATION
AAYXX
PublicationCentury 2000
PublicationDate 2021-04-01
PublicationDateYYYYMMDD 2021-04-01
PublicationDate_xml – month: 04
  year: 2021
  text: 2021-04-01
  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 2021
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
ref34
ref37
ref15
ref36
ref14
ref31
ref30
ref33
ref11
ref32
ref10
liu (ref22) 2015; 26
ref2
ref1
ref39
ref17
ref38
ref16
ref19
ref18
yan (ref12) 2018; 29
ref24
ref23
ref25
ref20
ref42
ref41
ref21
ref43
krstic (ref26) 1995; 222
liu (ref35) 2011; 22
ref28
ref27
ref29
ref8
ref7
ref9
ref4
ref3
ref6
ref5
ref40
References_xml – volume: 29
  start-page: 3588
  year: 2018
  ident: ref12
  article-title: Event-triggered asynchronous guaranteed cost control for Markov jump discrete-time neural networks with distributed delay and channel fading
  publication-title: IEEE Trans Neural Netw Learn Syst
  doi: 10.1109/TNNLS.2017.2732240
– ident: ref39
  doi: 10.1016/S0167-6911(97)00068-6
– ident: ref32
  doi: 10.1109/TFUZZ.2017.2686373
– ident: ref4
  doi: 10.1109/TNNLS.2018.2877195
– ident: ref33
  doi: 10.1109/TNNLS.2017.2760903
– ident: ref6
  doi: 10.1109/TNNLS.2016.2614002
– volume: 222
  year: 1995
  ident: ref26
  publication-title: Nonlinear and Adaptive Control Design
– ident: ref25
  doi: 10.1109/TAC.2016.2600340
– ident: ref42
  doi: 10.1016/j.automatica.2019.02.024
– ident: ref10
  doi: 10.1109/TCNS.2015.2428531
– ident: ref13
  doi: 10.1109/TAC.2016.2594204
– ident: ref38
  doi: 10.1109/TAC.2005.863501
– ident: ref31
  doi: 10.1109/TFUZZ.2017.2774185
– ident: ref2
  doi: 10.1109/TFUZZ.2018.2882173
– volume: 26
  start-page: 1789
  year: 2015
  ident: ref22
  article-title: Adaptive neural output feedback control of output-constrained nonlinear systems with unknown output nonlinearity
  publication-title: IEEE Trans Neural Netw Learn Syst
  doi: 10.1109/TNNLS.2015.2420661
– ident: ref21
  doi: 10.1016/j.automatica.2011.01.025
– ident: ref40
  doi: 10.1109/9.486648
– ident: ref28
  doi: 10.1109/TSMC.2017.2719629
– ident: ref23
  doi: 10.1109/TIE.2011.2107719
– ident: ref27
  doi: 10.1109/TCYB.2016.2594046
– ident: ref5
  doi: 10.1109/TNNLS.2015.2490168
– ident: ref30
  doi: 10.1109/TNN.2005.849824
– ident: ref24
  doi: 10.1109/TNNLS.2015.2412121
– ident: ref37
  doi: 10.1109/TSMC.2016.2557222
– ident: ref1
  doi: 10.1109/TNNLS.2018.2869375
– ident: ref36
  doi: 10.1109/TCYB.2017.2707178
– ident: ref3
  doi: 10.1109/TNNLS.2014.2305443
– ident: ref29
  doi: 10.1109/TIE.2013.2290757
– ident: ref20
  doi: 10.1109/TIE.2016.2597763
– ident: ref34
  doi: 10.1109/TFUZZ.2017.2750619
– ident: ref8
  doi: 10.1109/TAC.2007.904277
– ident: ref7
  doi: 10.1109/TFUZZ.2016.2633325
– ident: ref16
  doi: 10.1109/TSMC.2018.2883907
– volume: 22
  start-page: 1328
  year: 2011
  ident: ref35
  article-title: Adaptive neural output feedback controller design with reduced-order observer for a class of uncertain nonlinear SISO systems
  publication-title: IEEE Trans Neural Netw
  doi: 10.1109/TNN.2011.2159865
– ident: ref14
  doi: 10.1016/j.fss.2017.05.010
– ident: ref9
  doi: 10.1109/TAC.2014.2363603
– ident: ref41
  doi: 10.1109/72.822511
– ident: ref15
  doi: 10.1109/TSMC.2016.2531680
– ident: ref18
  doi: 10.1109/TNNLS.2016.2538779
– ident: ref11
  doi: 10.1109/TCYB.2018.2825984
– ident: ref19
  doi: 10.1109/TSMC.2015.2429555
– ident: ref17
  doi: 10.1109/TIE.2017.2701778
– ident: ref43
  doi: 10.1016/j.automatica.2019.05.033
SSID ssj0000605649
Score 2.654679
Snippet In this article, a dynamic event-triggered control scheme for a class of stochastic nonlinear systems with unknown input saturation and partially unmeasured...
SourceID proquest
pubmed
crossref
ieee
SourceType Aggregation Database
Index Database
Enrichment Source
Publisher
StartPage 1678
SubjectTerms Actuators
Adaptive control
Artificial neural networks
Control methods
Dynamic event-triggered mechanism (DEM)
improved neural network (NN)
input saturation
Multi-agent systems
Neural networks
Nonlinear control
Nonlinear dynamical systems
Nonlinear systems
Observers
Parameters
reduced-order observer
Saturation
Ships
stochastic nonlinear systems
Stochastic systems
Stochasticity
Title Reduced-Order Observer-Based Dynamic Event-Triggered Adaptive NN Control for Stochastic Nonlinear Systems Subject to Unknown Input Saturation
URI https://ieeexplore.ieee.org/document/9096510
https://www.ncbi.nlm.nih.gov/pubmed/32452775
https://www.proquest.com/docview/2509290427
https://www.proquest.com/docview/2406945798
Volume 32
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV1Lb9QwELZKT1woUB6BFhmJG3ib-BEnx7a0KoimErsr7S3yKyC1SqqSvfAf-M_MOA8hBIhblDgvzYz9zXjmG0LeyNSIIpUNy6RXTGolmfVNwYxsdFla43ykFLqs8ou1_LhRmx3ybq6FCSHE5LOwwMO4l-87t8VQ2VGJVCVYT3UPHLehVmuOp6SAy_OIdnmWc8aF3kw1Mml5tKqqT0vwBnm64CWA-AI7xAjcddSYYfjLkhR7rPwdbsZl53yPXE4fPGSbXC-2vV24779xOf7vHz0kD0b8SY8HhXlEdkL7mOxNvR3oaOr75Mdn5HQNnl0hNye9shi9DXfsBFY9T98PfezpGWZLshV4-F-w5yc99uYW509aVfR0SIKngIrpsu_cV4OU0LQauDkMnBzI0inMXRgMon1H1y3G-Fr6oYXPoUskHY2a84Ssz89WpxdsbN3AnFBZz8BLSS04vzZPrQKQ4RrubWMBrzluVN5YbQoAR2CicAhzv3AiSKU5KEejMmHFU7Lbdm14TmjOvZACe6J4J63NitJkJlOFLkPutM4Skk3Sq93Ia47tNW7q6N-kZR2FX6Pw61H4CXk733M7sHr8c_Q-Sm4eOQotIQeTktSj4X-rAVEC4MQGJgl5PV8Gk8V9GNOGbgtjYrWx0mWRkGeDcs3PnnTyxZ_f-ZLc55hUE1OHDshuf7cNh4CKevsqmsNPe5gG_w
linkProvider IEEE
linkToHtml http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV1Lb9QwELaqcoALBQo0UMBI3MDbxI88jqW02sJuKrG70t4ivwISKKlK9sJ_4D8z4zyEECBuUeK8NDP2N-OZbwh5JWMt8ljWLJFOMZkpyYyrc6ZlnRWF0dYFSqFlmc438v1WbffIm6kWxnsfks_8DA_DXr5r7Q5DZScFUpVgPdUtWPdV0ldrTRGVGJB5GvAuT1LOuMi2Y5VMXJysy3KxAn-QxzNeAIzPsUeMwH3HDHMMf1mUQpeVvwPOsPBcHJDl-Ml9vsmX2a4zM_v9NzbH__2ne-TugEDpaa8y98mebx6Qg7G7Ax2M_ZD8-Iisrt6xK2TnpFcG47f-hr2Fdc_Rd30ne3qO-ZJsDT7-J-z6SU-dvsYZlJYlPevT4CngYrrqWvtZIyk0LXt2Dg0ne7p0CrMXhoNo19JNg1G-hl428Dl0hbSjQXceks3F-fpszobmDcwKlXQM_JTYgPtr0tgogBm25s7UBhCb5Vqltcl0DvAIjBQOYfYXVnipMg7qUatEGPGI7Ddt448ITbkTUmBXFGelMUle6EQnKs8Kn9osSyKSjNKr7MBsjg02vlbBw4mLKgi_QuFXg_Aj8nq657rn9fjn6EOU3DRyEFpEjkclqQbT_1YBpgTIiS1MIvJyugxGizsxuvHtDsaEemOVFXlEHvfKNT171Mknf37nC3J7vl4uqsVl-eEpucMxxSYkEh2T_e5m558BRurM82AaPwFT2wpI
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=Reduced-Order+Observer-Based+Dynamic+Event-Triggered+Adaptive+NN+Control+for+Stochastic+Nonlinear+Systems+Subject+to+Unknown+Input+Saturation&rft.jtitle=IEEE+transaction+on+neural+networks+and+learning+systems&rft.au=Wang%2C+Lijie&rft.au=Chen%2C+C+L+Philip&rft.date=2021-04-01&rft.eissn=2162-2388&rft.volume=32&rft.issue=4&rft.spage=1678&rft_id=info:doi/10.1109%2FTNNLS.2020.2986281&rft_id=info%3Apmid%2F32452775&rft.externalDocID=32452775
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