Neural Network-Based Finite-Time Command Filtering Control for Switched Nonlinear Systems With Backlash-Like Hysteresis

This brief is concerned with the finite-time tracking control problem for switched nonlinear systems with arbitrary switching and hysteresis input. The neural networks are utilized to cope with the unknown nonlinear functions. To present the finite-time adaptive neural control strategy, a new criter...

Full description

Saved in:
Bibliographic Details
Published inIEEE transaction on neural networks and learning systems Vol. 32; no. 7; pp. 3268 - 3273
Main Authors Fu, Cheng, Wang, Qing-Guo, Yu, Jinpeng, Lin, Chong
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 01.07.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Adaptive control
Adaptive neural control
arbitrary switching
backlash-like hysteresis
Backstepping
command filtering
Control stability
Error compensation
finite-time
Hysteresis
Neural networks
Nonlinear control
Nonlinear systems
Stability analysis
Switched systems
Switches
Tracking control
Tracking errors
Online AccessGet full text

Cover

Abstract This brief is concerned with the finite-time tracking control problem for switched nonlinear systems with arbitrary switching and hysteresis input. The neural networks are utilized to cope with the unknown nonlinear functions. To present the finite-time adaptive neural control strategy, a new criterion of practical finite-time stability is first developed. Compared with the traditional command filter technique, the main advantage is that the improved error compensation signals are designed to remove the filtered error and the Levant differentiators are introduced to approximate the derivative of the virtual control signal. The finite-time adaptive neural controller is proposed via the new command filter backstepping technique, and the tracking error converges to a small neighborhood of the origin in finite time. Finally, the simulation results are provided to testify the validity of the proposed method.
AbstractList This brief is concerned with the finite-time tracking control problem for switched nonlinear systems with arbitrary switching and hysteresis input. The neural networks are utilized to cope with the unknown nonlinear functions. To present the finite-time adaptive neural control strategy, a new criterion of practical finite-time stability is first developed. Compared with the traditional command filter technique, the main advantage is that the improved error compensation signals are designed to remove the filtered error and the Levant differentiators are introduced to approximate the derivative of the virtual control signal. The finite-time adaptive neural controller is proposed via the new command filter backstepping technique, and the tracking error converges to a small neighborhood of the origin in finite time. Finally, the simulation results are provided to testify the validity of the proposed method.
This brief is concerned with the finite-time tracking control problem for switched nonlinear systems with arbitrary switching and hysteresis input. The neural networks are utilized to cope with the unknown nonlinear functions. To present the finite-time adaptive neural control strategy, a new criterion of practical finite-time stability is first developed. Compared with the traditional command filter technique, the main advantage is that the improved error compensation signals are designed to remove the filtered error and the Levant differentiators are introduced to approximate the derivative of the virtual control signal. The finite-time adaptive neural controller is proposed via the new command filter backstepping technique, and the tracking error converges to a small neighborhood of the origin in finite time. Finally, the simulation results are provided to testify the validity of the proposed method.This brief is concerned with the finite-time tracking control problem for switched nonlinear systems with arbitrary switching and hysteresis input. The neural networks are utilized to cope with the unknown nonlinear functions. To present the finite-time adaptive neural control strategy, a new criterion of practical finite-time stability is first developed. Compared with the traditional command filter technique, the main advantage is that the improved error compensation signals are designed to remove the filtered error and the Levant differentiators are introduced to approximate the derivative of the virtual control signal. The finite-time adaptive neural controller is proposed via the new command filter backstepping technique, and the tracking error converges to a small neighborhood of the origin in finite time. Finally, the simulation results are provided to testify the validity of the proposed method.
Author Lin, Chong
Fu, Cheng
Wang, Qing-Guo
Yu, Jinpeng
Author_xml – sequence: 1
  givenname: Cheng
  orcidid: 0000-0002-0122-452X
  surname: Fu
  fullname: Fu, Cheng
  email: fucheng1027@126.com
  organization: School of Automation, Qingdao University, Qingdao, China
– sequence: 2
  givenname: Qing-Guo
  orcidid: 0000-0002-3672-3716
  surname: Wang
  fullname: Wang, Qing-Guo
  email: wangq@uj.ac.za
  organization: Faculty of Engineering and the Built Environment, University of Johannesburg, Johannesburg, South Africa
– sequence: 3
  givenname: Jinpeng
  orcidid: 0000-0002-5432-1702
  surname: Yu
  fullname: Yu, Jinpeng
  email: yjp1109@hotmail.com
  organization: School of Automation, Qingdao University, Qingdao, China
– sequence: 4
  givenname: Chong
  orcidid: 0000-0002-7888-303X
  surname: Lin
  fullname: Lin, Chong
  email: linchong_2004@hotmail.com
  organization: School of Automation, Qingdao University, Qingdao, China
BookMark eNp9kbtuGzEQRYnAQfyIfyBuFkiTZhU-d8nSFvwChE1hBUlHcOlRRItLOiQFQX-fXctw4SLTzODOPQOC9xQdhRgAoS8EzwjB6vuy6xYPM4opnjGMlWzJB3RCSUNryqQ8epvb38foPOcnPFaDRcPVJ3TMaMuE4PgE7TrYJuOrDsoupk19ZTI8VjcuuAL10g1QzeMwmDBpvkBy4c-ohJKir1YxVQ87V-x6RLoYvAtgRmmfCwy5-uXKuroyduNNXtcLt4HqblolyC5_Rh9Xxmc4f-1n6OfN9XJ-Vy9-3N7PLxe1ZVSWWklsrQXZMMUltfax570hTKx43yirerCEEmw5AOO94qbpueCNkFgxImxr2Bn6drj7nOLfLeSiB5cteG8CxG3WlFPVStxINVq_vrM-xW0K4-s0FVwqhVmLR5c8uGyKOSdYaeuKKW76E-O8JlhP-eiXfPSUj37NZ0TpO_Q5ucGk_f-hiwPkAOANUEQwJRj7Bwn4nGc
CODEN ITNNAL
CitedBy_id crossref_primary_10_1016_j_cja_2024_07_041
crossref_primary_10_1109_TTE_2023_3320770
crossref_primary_10_1155_2020_2793580
crossref_primary_10_1109_TCYB_2021_3057127
crossref_primary_10_1016_j_ins_2022_04_048
crossref_primary_10_3390_s22228763
crossref_primary_10_1016_j_conengprac_2023_105559
crossref_primary_10_1016_j_jfranklin_2021_04_036
crossref_primary_10_1109_TFUZZ_2024_3392632
crossref_primary_10_1109_TNNLS_2022_3149894
crossref_primary_10_1016_j_amc_2022_126953
crossref_primary_10_3390_electronics12132771
crossref_primary_10_1109_TNNLS_2021_3104846
crossref_primary_10_3390_math11010031
crossref_primary_10_1016_j_ymssp_2023_110562
crossref_primary_10_1109_TFUZZ_2022_3228012
crossref_primary_10_1007_s40815_022_01405_w
crossref_primary_10_1109_TASE_2024_3443457
crossref_primary_10_1016_j_isatra_2023_03_007
crossref_primary_10_3390_fractalfract8060339
crossref_primary_10_1080_00207721_2023_2210148
crossref_primary_10_1109_TCYB_2021_3132587
crossref_primary_10_1016_j_jfranklin_2022_08_036
crossref_primary_10_1016_j_engappai_2025_110474
crossref_primary_10_1002_rnc_7799
crossref_primary_10_1016_j_isatra_2023_11_037
crossref_primary_10_3390_math12071070
crossref_primary_10_1109_TSMC_2021_3125772
crossref_primary_10_1109_TFUZZ_2023_3237337
crossref_primary_10_1007_s40435_023_01255_w
crossref_primary_10_1177_01423312211047116
crossref_primary_10_1007_s12555_022_0941_7
crossref_primary_10_1049_cth2_12517
crossref_primary_10_1016_j_jfranklin_2021_12_004
crossref_primary_10_1109_TNNLS_2022_3203074
crossref_primary_10_1177_01423312221110437
crossref_primary_10_1016_j_mejo_2021_105053
crossref_primary_10_1109_JAS_2023_123831
crossref_primary_10_1016_j_isatra_2022_04_050
crossref_primary_10_1109_TIE_2021_3070494
crossref_primary_10_1007_s00521_023_08418_y
crossref_primary_10_1016_j_isatra_2022_10_048
crossref_primary_10_1109_ACCESS_2025_3538260
crossref_primary_10_1016_j_isatra_2022_08_023
crossref_primary_10_1109_TFUZZ_2023_3331742
crossref_primary_10_1109_TNNLS_2021_3072552
crossref_primary_10_1007_s12555_021_0197_7
crossref_primary_10_1016_j_ejcon_2023_100799
crossref_primary_10_1109_TASE_2023_3263535
crossref_primary_10_1109_TMECH_2023_3250481
crossref_primary_10_1109_TSMC_2022_3211332
crossref_primary_10_1007_s11071_022_07472_w
crossref_primary_10_1016_j_actaastro_2022_10_002
crossref_primary_10_1007_s12555_021_0043_y
crossref_primary_10_1109_TFUZZ_2023_3259381
crossref_primary_10_1002_rnc_7517
crossref_primary_10_1002_acs_3681
crossref_primary_10_1007_s12190_024_02042_2
crossref_primary_10_1016_j_sigpro_2021_108305
crossref_primary_10_3934_math_2024048
crossref_primary_10_1002_rnc_7765
crossref_primary_10_1002_rnc_7247
crossref_primary_10_1007_s40815_023_01617_8
crossref_primary_10_1016_j_isatra_2023_02_024
crossref_primary_10_1002_asjc_3367
crossref_primary_10_1016_j_isatra_2023_12_034
crossref_primary_10_1109_TIE_2023_3269478
crossref_primary_10_1016_j_isatra_2023_10_002
crossref_primary_10_1080_0954898X_2023_2296115
crossref_primary_10_1016_j_ins_2021_04_097
crossref_primary_10_1016_j_neucom_2020_10_085
crossref_primary_10_1080_00207721_2023_2180783
crossref_primary_10_1088_2631_8695_ad63f6
crossref_primary_10_1016_j_isatra_2022_09_003
crossref_primary_10_1016_j_ins_2022_08_092
crossref_primary_10_1002_asjc_3111
crossref_primary_10_1080_03772063_2024_2394597
crossref_primary_10_1016_j_jfranklin_2022_10_042
crossref_primary_10_1109_TPEL_2022_3211412
crossref_primary_10_1016_j_jfranklin_2021_01_036
crossref_primary_10_1016_j_neucom_2022_09_034
crossref_primary_10_1109_ACCESS_2021_3069152
crossref_primary_10_1007_s40815_022_01314_y
crossref_primary_10_1080_03081079_2024_2364623
crossref_primary_10_1109_TCSI_2023_3342070
crossref_primary_10_1177_10775463221105698
crossref_primary_10_1016_j_ins_2021_02_050
crossref_primary_10_1002_rnc_6573
crossref_primary_10_1007_s12555_021_0433_1
crossref_primary_10_1016_j_neucom_2021_06_007
crossref_primary_10_1109_TCYB_2020_3032530
crossref_primary_10_1016_j_jfranklin_2022_10_057
crossref_primary_10_1109_TCYB_2022_3223894
crossref_primary_10_1007_s12555_021_0558_2
crossref_primary_10_1109_TCSII_2021_3109257
crossref_primary_10_1109_TNNLS_2022_3176625
crossref_primary_10_3390_photonics11020156
crossref_primary_10_1109_TFUZZ_2022_3174907
crossref_primary_10_1109_TSMC_2023_3296442
crossref_primary_10_1109_TCYB_2021_3108237
crossref_primary_10_1109_TII_2023_3300415
Cites_doi 10.1002/rnc.1624
10.1016/j.automatica.2010.06.050
10.1109/TNN.2004.824411
10.1109/TSMC.2017.2678760
10.1109/TAC.2009.2015562
10.1109/72.870049
10.1016/j.automatica.2018.03.033
10.1002/acs.2980
10.1137/S0363012997321358
10.1109/TCST.2011.2121907
10.1109/TFUZZ.2016.2574913
10.1109/TNN.2004.839354
10.1080/0020717031000099029
10.1016/j.automatica.2013.09.001
10.1109/TNN.2010.2042611
10.1109/TFUZZ.2017.2717804
10.1016/S1474-6670(17)50717-X
10.1109/9.895588
10.1109/TSMCB.2008.2006368
10.1109/TSMC.2019.2911115
10.1016/j.automatica.2005.07.001
10.1080/00207721003770569
10.1016/j.jfranklin.2016.12.029
10.1109/TSMC.2017.2696710
10.1016/j.sysconle.2004.09.006
10.1109/9.935058
10.1109/TNNLS.2014.2305717
10.1109/TSMC.2017.2675540
10.1049/iet-cta.2015.0635
10.1109/TSMC.2016.2557222
10.1016/j.automatica.2014.11.019
10.1109/TCYB.2016.2633367
10.1109/TSMC.2016.2606159
10.1016/j.automatica.2004.11.036
10.1109/TAC.2004.835398
10.1109/TNNLS.2014.2360933
10.1109/TNNLS.2019.2915376
10.1016/j.cnsns.2009.09.004
10.1109/TFUZZ.2016.2634162
10.1016/j.sysconle.2013.07.003
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
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.2020.3009871
DatabaseName IEEE All-Society Periodicals Package (ASPP) 2005–Present
IEEE All-Society Periodicals Package (ASPP) 1998–Present
IEEE Xplore
CrossRef
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
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
Database_xml – sequence: 1
  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 3273
ExternalDocumentID 10_1109_TNNLS_2020_3009871
9153953
Genre orig-research
GrantInformation_xml – fundername: National Key Research and the Development Plan
  grantid: 2017YFB1303503
– fundername: Oppenheimer Memorial Trust grant
– fundername: National Natural Science Foundation of China
  grantid: 61973179; 61673227
  funderid: 10.13039/501100001809
– fundername: National Research Foundation of South Africa
  grantid: 113340; 120106
– fundername: Taishan Scholar Special Project Fund
  grantid: TSQN20161026
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
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-c328t-980ccce8639482ccdb4ba135f4b69c9bec1210c4ee34b94a6b45465809315c7a3
IEDL.DBID RIE
ISSN 2162-237X
2162-2388
IngestDate Thu Jul 10 22:16:45 EDT 2025
Sun Jun 29 14:17:35 EDT 2025
Tue Jul 01 00:27:34 EDT 2025
Thu Apr 24 23:02:14 EDT 2025
Wed Aug 27 02:26:39 EDT 2025
IsPeerReviewed false
IsScholarly true
Issue 7
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-c328t-980ccce8639482ccdb4ba135f4b69c9bec1210c4ee34b94a6b45465809315c7a3
Notes ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ORCID 0000-0002-5432-1702
0000-0002-7888-303X
0000-0002-0122-452X
0000-0002-3672-3716
PMID 32735540
PQID 2548990370
PQPubID 85436
PageCount 6
ParticipantIDs proquest_miscellaneous_2429780689
crossref_primary_10_1109_TNNLS_2020_3009871
crossref_citationtrail_10_1109_TNNLS_2020_3009871
proquest_journals_2548990370
ieee_primary_9153953
ProviderPackageCode CITATION
AAYXX
PublicationCentury 2000
PublicationDate 2021-07-01
PublicationDateYYYYMMDD 2021-07-01
PublicationDate_xml – month: 07
  year: 2021
  text: 2021-07-01
  day: 01
PublicationDecade 2020
PublicationPlace Piscataway
PublicationPlace_xml – name: Piscataway
PublicationTitle IEEE transaction on neural networks and learning systems
PublicationTitleAbbrev TNNLS
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 ref35
ref13
ref34
ref12
ref37
ref15
ref36
ref14
ref31
ref30
ref33
ref11
ren (ref29) 2009; 39
ref32
ref10
ref1
ref39
ref17
ref38
ref16
ref19
ref18
krstic (ref2) 1995
ref24
ref23
liu (ref5) 2014; 25
ref26
ref25
ref20
ref41
ref22
ref21
ref28
ref27
ref8
ref7
ref9
ref4
ref3
ref6
ref40
References_xml – ident: ref41
  doi: 10.1002/rnc.1624
– ident: ref3
  doi: 10.1016/j.automatica.2010.06.050
– ident: ref37
  doi: 10.1109/TNN.2004.824411
– ident: ref10
  doi: 10.1109/TSMC.2017.2678760
– ident: ref15
  doi: 10.1109/TAC.2009.2015562
– ident: ref36
  doi: 10.1109/72.870049
– ident: ref28
  doi: 10.1016/j.automatica.2018.03.033
– ident: ref7
  doi: 10.1002/acs.2980
– ident: ref21
  doi: 10.1137/S0363012997321358
– ident: ref16
  doi: 10.1109/TCST.2011.2121907
– ident: ref20
  doi: 10.1109/TFUZZ.2016.2574913
– ident: ref39
  doi: 10.1109/TNN.2004.839354
– ident: ref35
  doi: 10.1080/0020717031000099029
– year: 1995
  ident: ref2
  publication-title: Nonlinear and Adaptive Control Design
– ident: ref23
  doi: 10.1016/j.automatica.2013.09.001
– ident: ref40
  doi: 10.1109/TNN.2010.2042611
– ident: ref11
  doi: 10.1109/TFUZZ.2017.2717804
– ident: ref30
  doi: 10.1016/S1474-6670(17)50717-X
– ident: ref33
  doi: 10.1109/9.895588
– volume: 39
  start-page: 431
  year: 2009
  ident: ref29
  article-title: Adaptive neural control for a class of uncertain nonlinear systems in pure-feedback form with hysteresis input
  publication-title: IEEE Trans Syst Man Cybern B Cybern
  doi: 10.1109/TSMCB.2008.2006368
– ident: ref19
  doi: 10.1109/TSMC.2019.2911115
– ident: ref22
  doi: 10.1016/j.automatica.2005.07.001
– ident: ref26
  doi: 10.1080/00207721003770569
– ident: ref34
  doi: 10.1016/j.jfranklin.2016.12.029
– ident: ref13
  doi: 10.1109/TSMC.2017.2696710
– ident: ref1
  doi: 10.1016/j.sysconle.2004.09.006
– ident: ref24
  doi: 10.1109/9.935058
– volume: 25
  start-page: 2129
  year: 2014
  ident: ref5
  article-title: Adaptive neural control for a class of nonlinear time-varying delay systems with unknown hysteresis
  publication-title: IEEE Trans Neural Netw Learn Syst
  doi: 10.1109/TNNLS.2014.2305717
– ident: ref14
  doi: 10.1109/TSMC.2017.2675540
– ident: ref27
  doi: 10.1049/iet-cta.2015.0635
– ident: ref9
  doi: 10.1109/TSMC.2016.2557222
– ident: ref12
  doi: 10.1016/j.automatica.2014.11.019
– ident: ref17
  doi: 10.1109/TCYB.2016.2633367
– ident: ref8
  doi: 10.1109/TSMC.2016.2606159
– ident: ref25
  doi: 10.1016/j.automatica.2004.11.036
– ident: ref31
  doi: 10.1109/TAC.2004.835398
– ident: ref6
  doi: 10.1109/TNNLS.2014.2360933
– ident: ref38
  doi: 10.1109/TNNLS.2019.2915376
– ident: ref32
  doi: 10.1016/j.cnsns.2009.09.004
– ident: ref18
  doi: 10.1109/TFUZZ.2016.2634162
– ident: ref4
  doi: 10.1016/j.sysconle.2013.07.003
SSID ssj0000605649
Score 2.6379101
Snippet This brief is concerned with the finite-time tracking control problem for switched nonlinear systems with arbitrary switching and hysteresis input. The neural...
SourceID proquest
crossref
ieee
SourceType Aggregation Database
Enrichment Source
Index Database
Publisher
StartPage 3268
SubjectTerms Adaptive control
Adaptive neural control
arbitrary switching
backlash-like hysteresis
Backstepping
command filtering
Control stability
Error compensation
finite-time
Hysteresis
Neural networks
Nonlinear control
Nonlinear systems
Stability analysis
Switched systems
Switches
Tracking control
Tracking errors
Title Neural Network-Based Finite-Time Command Filtering Control for Switched Nonlinear Systems With Backlash-Like Hysteresis
URI https://ieeexplore.ieee.org/document/9153953
https://www.proquest.com/docview/2548990370
https://www.proquest.com/docview/2429780689
Volume 32
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV3db9QwDLe2PfHCBgNxMFCQeIPc0iZtk0c2cTqhrS_bxL1VSZrTqpt6iPU0aX89dvohBAjx1Cpxq1a2Yzuxfwb4UOd18Cr1vNa24LTgcS2l5Vqtw3ptrZA11Q5flvnyRn1dZas9-DTVwoQQYvJZmNNtPMuvt35HW2WnBtXTZHIf9jFw62u1pv0UgX55Hr3dNMlTnspiNdbICHN6XZYXVxgNphikEoRmQR1iJJruLG57_GKSYo-VPxbmaG0Wh3A5fmefZLKZ7zo394-_QTj-748cwdPB7WSfezl5BnuhfQ6HY0sHNmj4MTwQWAcSln12OD9DI1ezRUOeKadyEUYVJbalMTpnR8PHzvtsd4buL7t6aEgMalb2EBwWh3pMdPat6W7ZmfUb9Ndv-UWzCWxJUxjvN_cv4Gbx5fp8yYfeDNzLVHfcaOG9DxodHKVT72unnE1ktlYuN96gZBAymVchSOWMsrlT1HZdCyOTzBdWvoSDdtuGV8DyxGOUZp0KVFhljJV4cdpYkYSilm4Gycieyg_A5dQ_466KAYwwVeRuRdytBu7O4OP0zPcetuOf1MfEo4lyYM8MTkYpqAbNvq8woMYQVchCzOD9NI06SQcttg3bHdKgzBda5Nq8_vub38CTlHJjYtrvCRx0P3bhLTo3nXsXpfoncd_0sg
linkProvider IEEE
linkToHtml http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV3db9MwELfGeIAXBgy0wgAj8QbuHNtJ7Ec2URVo87JO9C2yHVeLilLEUk3ir-fO-RAChHhKZF-iRHfn-519H4S8qbIqeCU8q7TNGS54TEtpmVabsNlYy2WFucPLIptfqU_rdH1A3o25MCGEGHwWpngbz_Krnd_jVtmZAfU0qbxD7oLdT0WXrTXuqHBA5lnEuyLJBBMyXw9ZMtycrYpicQn-oAA3FYto5tgjRoLxTuPGxy9GKXZZ-WNpjvZmdkSWw5d2YSbb6b51U__jtyKO__srD8mDHnjS952kPCIHoXlMjoamDrTX8WNyi-U6gLDo4sPZOZi5is5qxKYME0Yo5pTYBsfwpB1MH73o4t0pAGB6eVujIFS06IpwWBjqqqLTL3V7Tc-t3wJiv2aLehvoHKfA469vnpCr2YfVxZz13RmYl0K3zGjuvQ8aII7SwvvKKWcTmW6Uy4w3IBtYm8yrEKRyRtnMKWy8rrmRSepzK5-Sw2bXhBNCs8SDn2adCphaZYyVcHHaWJ6EvJJuQpKBPaXvS5djB42vZXRhuCkjd0vkbtlzd0Lejs986wp3_JP6GHk0UvbsmZDTQQrKXrdvSnCpwUnlMucT8nqcBq3EoxbbhN0eaEDqc80zbZ79_c2vyL35arkoFx-Lz8_JfYGRMjEI-JQctt_34QVAnda9jBL-E15F9_w
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=Neural+Network-Based+Finite-Time+Command+Filtering+Control+for+Switched+Nonlinear+Systems+With+Backlash-Like+Hysteresis&rft.jtitle=IEEE+transaction+on+neural+networks+and+learning+systems&rft.au=Fu%2C+Cheng&rft.au=Wang%2C+Qing-Guo&rft.au=Yu%2C+Jinpeng&rft.au=Lin%2C+Chong&rft.date=2021-07-01&rft.issn=2162-2388&rft.eissn=2162-2388&rft.volume=32&rft.issue=7&rft.spage=3268&rft_id=info:doi/10.1109%2FTNNLS.2020.3009871&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