Event-Driven Guaranteed Cost Control Design for Nonlinear Systems With Actuator Faults via Reinforcement Learning Algorithm

This article presents a novel event-driven guaranteed cost control method for nonlinear systems subject to actuator faults. For the purpose of handling the problem of actuator faults and obtaining the event-driven approximate optimal guaranteed cost control approach for general nonlinear dynamics, t...

Full description

Saved in:

Bibliographic Details
Published in:	IEEE transactions on systems, man, and cybernetics. Systems Vol. 50; no. 11; pp. 4135 - 4150
Main Authors:	Zhang, Huaguang, Liang, Yuling, Su, Hanguang, Liu, Chong
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.11.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Actuators Algorithms Approximation algorithms Control methods Control systems Cost function Dynamical systems Event-driven control fault tolerant control Faults guaranteed cost control Heuristic algorithms Machine learning Neural networks Nonlinear control Nonlinear dynamics Nonlinear systems Process control reinforcement learning (RL) Sliding mode control sliding mode control (SMC)
ISSN:	2168-2216, 2168-2232
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Abstract	This article presents a novel event-driven guaranteed cost control method for nonlinear systems subject to actuator faults. For the purpose of handling the problem of actuator faults and obtaining the event-driven approximate optimal guaranteed cost control approach for general nonlinear dynamics, the reinforcement learning (RL) algorithm is utilized to develop a sliding-mode control (SMC) strategy. To begin with, the unknown faults can be estimated by designing a fault observer. Meanwhile, an SMC technique is presented aiming at countering the effect of abrupt faults. In addition, the optimal performance of the equivalent sliding mode dynamics is considered, then an event-driven guaranteed cost control mechanism is implemented by using RL principle. In the control process, a general cost function, which has a simpler structure, is given to reduce the computation complexity. At the same time, a modified cost function is approximated to obtain optimal guaranteed cost control by using a single critic neural network (NN). In addition, a modified weight update law for critic NN is presented to relax the persistence of excitation (PE) condition. Moreover, a newly triggering condition, which is easy to be implemented, is designed, and the critic NN update law makes sure that the system states are stable. Furthermore, in light of the Lyapunov analysis, it is demonstrated that the developed event-driven control method guarantees the uniformly ultimately bounded (UUB) property of all the signals. Finally, three simulation results are given to validate the designed control method.
AbstractList	This article presents a novel event-driven guaranteed cost control method for nonlinear systems subject to actuator faults. For the purpose of handling the problem of actuator faults and obtaining the event-driven approximate optimal guaranteed cost control approach for general nonlinear dynamics, the reinforcement learning (RL) algorithm is utilized to develop a sliding-mode control (SMC) strategy. To begin with, the unknown faults can be estimated by designing a fault observer. Meanwhile, an SMC technique is presented aiming at countering the effect of abrupt faults. In addition, the optimal performance of the equivalent sliding mode dynamics is considered, then an event-driven guaranteed cost control mechanism is implemented by using RL principle. In the control process, a general cost function, which has a simpler structure, is given to reduce the computation complexity. At the same time, a modified cost function is approximated to obtain optimal guaranteed cost control by using a single critic neural network (NN). In addition, a modified weight update law for critic NN is presented to relax the persistence of excitation (PE) condition. Moreover, a newly triggering condition, which is easy to be implemented, is designed, and the critic NN update law makes sure that the system states are stable. Furthermore, in light of the Lyapunov analysis, it is demonstrated that the developed event-driven control method guarantees the uniformly ultimately bounded (UUB) property of all the signals. Finally, three simulation results are given to validate the designed control method.
Author	Liu, Chong Liang, Yuling Zhang, Huaguang Su, Hanguang
Author_xml	– sequence: 1 givenname: Huaguang orcidid: 0000-0002-0647-4050 surname: Zhang fullname: Zhang, Huaguang email: hgzhang@ieee.org organization: State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, China – sequence: 2 givenname: Yuling orcidid: 0000-0002-9256-2005 surname: Liang fullname: Liang, Yuling email: 18804038702@163.com organization: School of Information Science and Engineering, Northeastern University, Shenyang, China – sequence: 3 givenname: Hanguang orcidid: 0000-0003-1356-4158 surname: Su fullname: Su, Hanguang email: suhanguang@sina.com organization: School of Information Science and Engineering, Northeastern University, Shenyang, China – sequence: 4 givenname: Chong orcidid: 0000-0001-9842-6955 surname: Liu fullname: Liu, Chong email: liuchong_hebei@126.com organization: School of Information Science and Engineering, Northeastern University, Shenyang, China
BookMark	eNp9kE1LAzEQhoMoWLU_QLwEPG_NxzabHEtrq1AVtOJxSXdna8o2qUlWKP55UyoePHjJBOZ9ZpjnDB1bZwGhS0oGlBJ1s3h5GA8YoWrAVC7ksDhCPUaFzBjj7Pj3T8Up6oewJoRQJgUnooe-bj_BxmziTap41mmvbQSo8diFmB4bvWvxBIJZWdw4jx-dbY0F7fHLLkTYBPxm4jseVbHTMfWnumtjwJ9G42cwNiEVbNIKPE-MNXaFR-3K-cRsLtBJo9sA_Z96jl6nt4vxXTZ_mt2PR_OsYorHrBoCkxURTZ6D5pVqVF3ntJCKF5oBqCU0WipRNEQv01FSFHW9rLnSuc4bRRQ_R9eHuVvvPjoIsVy7ztu0smT5kFLFciJSqjikKu9C8NCUlYk6mr0CbdqSknIvu9zLLveyyx_ZiaR_yK03G-13_zJXB8YAwG9eSsm4LPg3msaOew
CODEN	ITSMFE
CitedBy_id	crossref_primary_10_1109_TSMC_2024_3495821 crossref_primary_10_1109_TNNLS_2021_3085555 crossref_primary_10_1109_TSMC_2024_3497961 crossref_primary_10_1109_TASE_2025_3573908 crossref_primary_10_1109_TFUZZ_2020_3037959 crossref_primary_10_1016_j_arcontrol_2022_03_005 crossref_primary_10_1109_TCYB_2025_3549821 crossref_primary_10_1016_j_cnsns_2025_108933 crossref_primary_10_1109_TCNS_2022_3204795 crossref_primary_10_1016_j_ins_2024_120484 crossref_primary_10_1002_rnc_6234 crossref_primary_10_1109_TCSII_2021_3103856 crossref_primary_10_1016_j_amc_2020_125881 crossref_primary_10_1109_TCYB_2024_3440004 crossref_primary_10_1109_TCSI_2021_3135911 crossref_primary_10_1109_TCSII_2022_3195934 crossref_primary_10_1109_TSMC_2022_3228738 crossref_primary_10_1016_j_amc_2020_125871 crossref_primary_10_1109_ACCESS_2021_3140076 crossref_primary_10_1109_TNNLS_2021_3104839 crossref_primary_10_1007_s00521_021_06539_w crossref_primary_10_1109_TCYB_2023_3273848 crossref_primary_10_1007_s11063_022_11085_0 crossref_primary_10_1002_acs_3945 crossref_primary_10_1109_TSMC_2021_3098025 crossref_primary_10_1109_TNNLS_2022_3221105 crossref_primary_10_1109_TCSII_2021_3067708 crossref_primary_10_1016_j_jfranklin_2025_108032 crossref_primary_10_3390_math12040503 crossref_primary_10_1016_j_amc_2021_126336 crossref_primary_10_1109_TNNLS_2022_3154028 crossref_primary_10_1109_TSMC_2022_3166930 crossref_primary_10_1109_TSMC_2022_3196502 crossref_primary_10_1109_ACCESS_2020_3028064 crossref_primary_10_1109_TSMC_2020_3042876 crossref_primary_10_1016_j_ins_2020_10_056 crossref_primary_10_1177_10775463211043357 crossref_primary_10_1002_asjc_3780 crossref_primary_10_1016_j_fss_2023_108735 crossref_primary_10_1002_rnc_70152 crossref_primary_10_1109_TR_2024_3407090 crossref_primary_10_1002_oca_2775 crossref_primary_10_1016_j_neucom_2023_126529 crossref_primary_10_1109_TSMC_2024_3406777 crossref_primary_10_1109_TSMC_2021_3131495 crossref_primary_10_1109_TSMC_2021_3082549 crossref_primary_10_1109_JAS_2024_124317 crossref_primary_10_1016_j_ins_2024_120756 crossref_primary_10_1109_TNNLS_2022_3140478 crossref_primary_10_1109_TNNLS_2021_3116464 crossref_primary_10_1016_j_ins_2024_120717 crossref_primary_10_1109_TFUZZ_2023_3303624 crossref_primary_10_1109_TSMC_2024_3524399 crossref_primary_10_1109_TAI_2024_3375828 crossref_primary_10_3390_electronics12051127 crossref_primary_10_1109_TASE_2023_3317902 crossref_primary_10_1109_TCYB_2025_3590340 crossref_primary_10_1002_rnc_6152 crossref_primary_10_1002_oca_2834 crossref_primary_10_1016_j_neunet_2024_106737 crossref_primary_10_1109_TAC_2021_3079279 crossref_primary_10_1007_s00521_021_06527_0 crossref_primary_10_1007_s40815_024_01891_0 crossref_primary_10_1109_TASE_2025_3571379 crossref_primary_10_1109_TCYB_2020_3035907 crossref_primary_10_1002_oca_2792 crossref_primary_10_1002_oca_2791 crossref_primary_10_1016_j_cnsns_2024_107928 crossref_primary_10_1002_oca_2794 crossref_primary_10_1109_TCNS_2022_3181550 crossref_primary_10_3390_knowledge3030028 crossref_primary_10_1007_s11071_024_10209_6 crossref_primary_10_1109_ACCESS_2019_2962583 crossref_primary_10_1109_TVT_2024_3406347 crossref_primary_10_1109_TCSII_2021_3049243 crossref_primary_10_1109_TSMC_2023_3257415 crossref_primary_10_1109_TSMC_2024_3421658 crossref_primary_10_1016_j_engappai_2024_109734 crossref_primary_10_1109_TSMC_2022_3224432 crossref_primary_10_1109_TNNLS_2020_3030127 crossref_primary_10_1109_TSMC_2022_3210709 crossref_primary_10_1109_TSMC_2023_3331150 crossref_primary_10_1109_TNNLS_2021_3136939 crossref_primary_10_1016_j_engappai_2025_112221 crossref_primary_10_1016_j_neucom_2023_126502 crossref_primary_10_1109_TII_2024_3384623 crossref_primary_10_1109_TFUZZ_2021_3094716 crossref_primary_10_1016_j_engappai_2025_110207 crossref_primary_10_1002_rnc_5364 crossref_primary_10_1109_TSMC_2021_3089944 crossref_primary_10_1007_s11071_022_07828_2 crossref_primary_10_1109_TFUZZ_2021_3098341 crossref_primary_10_1002_rnc_6318 crossref_primary_10_1109_TNNLS_2022_3214681 crossref_primary_10_1109_TII_2020_3042631 crossref_primary_10_1016_j_neucom_2022_08_011 crossref_primary_10_1109_TCSII_2021_3115487 crossref_primary_10_1016_j_amc_2022_127082 crossref_primary_10_1109_TSMC_2023_3283017 crossref_primary_10_1109_TAI_2023_3346334 crossref_primary_10_1109_JAS_2021_1004359 crossref_primary_10_1109_TFUZZ_2023_3273566 crossref_primary_10_1016_j_neucom_2020_11_014 crossref_primary_10_1049_cth2_12545 crossref_primary_10_3390_sym14061264
Cites_doi	10.1109/TCYB.2017.2653800 10.1016/0005-1098(88)90027-1 10.1109/TSMC.2017.2672664 10.1109/CDC.1996.577594 10.1109/TCYB.2018.2823199 10.1109/TSMCB.2011.2166384 10.1109/TNNLS.2015.2472974 10.1109/TNNLS.2017.2660070 10.1109/TPEL.2010.2070081 10.1016/j.automatica.2018.03.066 10.1109/37.387613 10.1002/9781118029176 10.1109/TSMC.2017.2744671 10.1016/j.automatica.2012.09.019 10.1109/TSMC.2017.2774602 10.1109/TAC.2010.2089650 10.1109/TNNLS.2016.2642128 10.1109/TNNLS.2016.2614002 10.1016/j.fss.2006.09.010 10.1049/iet-cta.2015.1081 10.1049/iet-cta.2009.0086 10.1109/TNNLS.2013.2276571 10.1016/j.automatica.2013.09.043 10.1016/j.automatica.2017.03.013 10.1109/TSMC.2016.2634080 10.1109/TNNLS.2013.2281663 10.1007/s00521-018-3537-7 10.1016/j.isatra.2015.02.004 10.1109/TCYB.2014.2319577 10.1109/TNNLS.2018.2844165 10.1109/TNNLS.2018.2869375 10.1109/ACC.2011.5991481 10.1016/j.automatica.2015.10.039 10.1016/j.automatica.2018.10.030 10.1109/TFUZZ.2011.2181181 10.1109/IJCNN.2015.7280471 10.1016/j.automatica.2014.10.056 10.1109/TNNLS.2018.2817256 10.1016/j.automatica.2010.10.033 10.1016/j.cirp.2011.03.041 10.1109/TCYB.2014.2357896 10.1109/TII.2018.2862436 10.1109/TNNLS.2012.2196708 10.1109/TCYB.2016.2523878 10.1016/j.mechmachtheory.2011.06.003 10.1109/TCYB.2018.2865174 10.1109/TII.2012.2223220 10.1109/TCYB.2015.2417170 10.1016/j.neucom.2018.09.011 10.1016/j.neucom.2017.10.041 10.1016/j.neucom.2017.04.008 10.1109/TNNLS.2018.2791419 10.1080/00207721.2018.1479004 10.1007/978-3-662-05344-7_7 10.1109/TNNLS.2013.2292704 10.1109/MCS.2013.2249417 10.1049/iet-cta.2012.0696 10.1109/TAC.1972.1100037 10.1016/j.automatica.2014.03.015 10.1016/j.ymssp.2015.11.020 10.1109/TSTE.2019.2894633 10.1109/TFUZZ.2017.2717801
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 7SC 7SP 7TB 8FD FR3 H8D JQ2 L7M L~C L~D
DOI	10.1109/TSMC.2019.2946857
DatabaseName	IEEE Xplore (IEEE) IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef Computer and Information Systems Abstracts Electronics & Communications Abstracts Mechanical & Transportation Engineering Abstracts Technology Research Database Engineering Research Database Aerospace Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional
DatabaseTitle	CrossRef Aerospace Database Technology Research Database Computer and Information Systems Abstracts – Academic Mechanical & Transportation Engineering Abstracts Electronics & Communications Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Engineering Research Database Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Professional
DatabaseTitleList	Aerospace Database
Database_xml	– sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering
EISSN	2168-2232
EndPage	4150
ExternalDocumentID	10_1109_TSMC_2019_2946857 8882387
Genre	orig-research
GrantInformation_xml	– fundername: National Natural Science Foundation of China grantid: 61433004; 61627809; 61621004 funderid: 10.13039/501100001809 – fundername: Liaoning Revitalization Talents Program grantid: XLYC1801005
GroupedDBID	0R~ 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACGFS ACIWK AGQYO AGSQL AHBIQ AKJIK AKQYR ALMA_UNASSIGNED_HOLDINGS BEFXN BFFAM BGNUA BKEBE BPEOZ EBS EJD HZ~ IFIPE IPLJI JAVBF M43 O9- OCL PQQKQ RIA RIE RNS AAYXX CITATION 7SC 7SP 7TB 8FD FR3 H8D JQ2 L7M L~C L~D
ID	FETCH-LOGICAL-c293t-c5e28c06f44ea3c9f9dd4178937a2ee9befa8967f0ab630867ddbd39a4a4f9093
IEDL.DBID	RIE
ISICitedReferencesCount	114
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000578826300018&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	2168-2216
IngestDate	Sun Jun 29 16:41:02 EDT 2025 Sat Nov 29 03:45:36 EST 2025 Tue Nov 18 21:42:11 EST 2025 Wed Aug 27 02:30:34 EDT 2025
IsPeerReviewed	true
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-c293t-c5e28c06f44ea3c9f9dd4178937a2ee9befa8967f0ab630867ddbd39a4a4f9093
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0000-0003-1356-4158 0000-0002-0647-4050 0000-0002-9256-2005 0000-0001-9842-6955
PQID	2451192406
PQPubID	75739
PageCount	16
ParticipantIDs	crossref_citationtrail_10_1109_TSMC_2019_2946857 ieee_primary_8882387 proquest_journals_2451192406 crossref_primary_10_1109_TSMC_2019_2946857
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	New York
PublicationPlace_xml	– name: New York
PublicationTitle	IEEE transactions on systems, man, and cybernetics. Systems
PublicationTitleAbbrev	TSMC
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	ref13 ref57 ref12 ref56 ref15 ref59 ref14 ref58 ref53 ref52 ref11 ref55 ref10 ref54 ref17 Xu (ref29) 2014; 25 ref16 ref19 ref18 ref51 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 ref60 ref62 ref61
References_xml	– ident: ref36 doi: 10.1109/TCYB.2017.2653800 – ident: ref19 doi: 10.1016/0005-1098(88)90027-1 – ident: ref8 doi: 10.1109/TSMC.2017.2672664 – ident: ref20 doi: 10.1109/CDC.1996.577594 – ident: ref35 doi: 10.1109/TCYB.2018.2823199 – ident: ref30 doi: 10.1109/TSMCB.2011.2166384 – ident: ref43 doi: 10.1109/TNNLS.2015.2472974 – ident: ref49 doi: 10.1109/TNNLS.2017.2660070 – ident: ref2 doi: 10.1109/TPEL.2010.2070081 – ident: ref18 doi: 10.1016/j.automatica.2018.03.066 – ident: ref47 doi: 10.1109/37.387613 – ident: ref31 doi: 10.1002/9781118029176 – ident: ref21 doi: 10.1109/TSMC.2017.2744671 – ident: ref48 doi: 10.1016/j.automatica.2012.09.019 – ident: ref32 doi: 10.1109/TSMC.2017.2774602 – ident: ref45 doi: 10.1109/TAC.2010.2089650 – ident: ref60 doi: 10.1109/TNNLS.2016.2642128 – ident: ref34 doi: 10.1109/TNNLS.2016.2614002 – ident: ref9 doi: 10.1016/j.fss.2006.09.010 – ident: ref37 doi: 10.1049/iet-cta.2015.1081 – ident: ref13 doi: 10.1049/iet-cta.2009.0086 – ident: ref50 doi: 10.1109/TNNLS.2013.2276571 – ident: ref56 doi: 10.1016/j.automatica.2013.09.043 – ident: ref25 doi: 10.1016/j.automatica.2017.03.013 – ident: ref62 doi: 10.1109/TSMC.2016.2634080 – ident: ref53 doi: 10.1109/TNNLS.2013.2281663 – ident: ref28 doi: 10.1007/s00521-018-3537-7 – ident: ref16 doi: 10.1016/j.isatra.2015.02.004 – ident: ref42 doi: 10.1109/TCYB.2014.2319577 – ident: ref26 doi: 10.1109/TNNLS.2018.2844165 – ident: ref11 doi: 10.1109/TNNLS.2018.2869375 – ident: ref58 doi: 10.1109/ACC.2011.5991481 – ident: ref59 doi: 10.1016/j.automatica.2015.10.039 – ident: ref10 doi: 10.1016/j.automatica.2018.10.030 – ident: ref12 doi: 10.1109/TFUZZ.2011.2181181 – ident: ref52 doi: 10.1109/IJCNN.2015.7280471 – ident: ref54 doi: 10.1016/j.automatica.2014.10.056 – ident: ref55 doi: 10.1109/TNNLS.2018.2817256 – ident: ref41 doi: 10.1016/j.automatica.2010.10.033 – ident: ref4 doi: 10.1016/j.cirp.2011.03.041 – ident: ref39 doi: 10.1109/TCYB.2014.2357896 – ident: ref23 doi: 10.1109/TII.2018.2862436 – ident: ref57 doi: 10.1109/TNNLS.2012.2196708 – ident: ref33 doi: 10.1109/TCYB.2016.2523878 – ident: ref15 doi: 10.1016/j.mechmachtheory.2011.06.003 – ident: ref22 doi: 10.1109/TCYB.2018.2865174 – ident: ref46 doi: 10.1109/TII.2012.2223220 – ident: ref61 doi: 10.1109/TCYB.2015.2417170 – ident: ref27 doi: 10.1016/j.neucom.2018.09.011 – ident: ref44 doi: 10.1016/j.neucom.2017.10.041 – ident: ref51 doi: 10.1016/j.neucom.2017.04.008 – ident: ref40 doi: 10.1109/TNNLS.2018.2791419 – ident: ref14 doi: 10.1080/00207721.2018.1479004 – ident: ref5 doi: 10.1007/978-3-662-05344-7_7 – volume: 25 start-page: 635 issue: 3 year: 2014 ident: ref29 article-title: Reinforcement learning output feedback NN control using deterministic learning technique publication-title: IEEE Trans. Neural Netw. Learn. Syst. doi: 10.1109/TNNLS.2013.2292704 – ident: ref1 doi: 10.1109/MCS.2013.2249417 – ident: ref6 doi: 10.1049/iet-cta.2012.0696 – ident: ref38 doi: 10.1109/TAC.1972.1100037 – ident: ref24 doi: 10.1016/j.automatica.2014.03.015 – ident: ref17 doi: 10.1016/j.ymssp.2015.11.020 – ident: ref3 doi: 10.1109/TSTE.2019.2894633 – ident: ref7 doi: 10.1109/TFUZZ.2017.2717801
SSID	ssj0001286306
Score	2.4932134
Snippet	This article presents a novel event-driven guaranteed cost control method for nonlinear systems subject to actuator faults. For the purpose of handling the...
SourceID	proquest crossref ieee
SourceType	Aggregation Database Enrichment Source Index Database Publisher
StartPage	4135
SubjectTerms	Actuators Algorithms Approximation algorithms Control methods Control systems Cost function Dynamical systems Event-driven control fault tolerant control Faults guaranteed cost control Heuristic algorithms Machine learning Neural networks Nonlinear control Nonlinear dynamics Nonlinear systems Process control reinforcement learning (RL) Sliding mode control sliding mode control (SMC)
Title	Event-Driven Guaranteed Cost Control Design for Nonlinear Systems With Actuator Faults via Reinforcement Learning Algorithm
URI	https://ieeexplore.ieee.org/document/8882387 https://www.proquest.com/docview/2451192406
Volume	50
WOSCitedRecordID	wos000578826300018&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: 2168-2232 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001286306 issn: 2168-2216 databaseCode: RIE dateStart: 20130101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LTxsxEB4B6gEOpS0gUh7yoaeKhY3X8drHKBBxaKOKR8tt5bVnIVJI0GbDpX--Hq-JIoEqcVntwSOt5pv1zNgz3wB8k7Z0XKM3Xu5sIrJKJCazJhGIRAhmKhXmp_z-kY9G6u5O_1qDk2UvDCKG4jM8pddwl-9mdkFHZWc-W_MeJl-H9TyXba_WynmKklkYpcm70oPvn_ESs5vqs5vrnwOq49KnXAupyBmtuKEwV-XVZhw8zHD7fd_2CT7GSJL1W-g_wxpOv8DWCr_gDvy9oGrG5LymLY2RNZAe0bHBbN6wQVulzs5DEQfz0SsbtcQZpmaRyZz9GTcPrE9dJj45Z0OzmDRz9jw27AoD56oNx4ss0rTes_7kflZ7mcdduB1e3AwukzhtIbHe5TeJ7SFXNpWVEOjh0pV2TnRzimcMR9QlVkZpmVepKb2mlcydK12mjTCi0qnO9mBjOpviPrDMqExK7rqlD1BSp3SPVz4t62VpKWWpTAfSF-UXNlKR00SMSRFSklQXhFdBeBURrw58X4o8tTwc_1u8QwAtF0ZsOnD4gnAR_9R5wYmgTVNc8_VtqQPY5JRjh_7DQ9ho6gUewQf73Izn9XEwwn92GtsR
linkProvider	IEEE
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3fb9MwED6NgQQ88GtDFAb4gSdENtdxHfux6lYN0VUICuwtcuzLqFRalKZ74Z_H53hVJRASL1EefFJ038V3Z999B_BGucoLg8F4hXeZzGuZ2dzZTCISIZitdZyf8nVSTKf68tJ83IN3214YRIzFZ3hMr_Eu36_cho7KTkK2FjxMcQtuD6QUvOvW2jlR0SqPwzRFXwX4wzNdY_a5OZl9vhhRJZc5FkYqTe5oxxHFySp_bMfRx4wf_t_XPYIHKZZkww78x7CHyydwf4dh8AB-nVE9Y3ba0KbGyB5Ik-jZaLVu2airU2ensYyDhfiVTTvqDNuwxGXOvs3b72xIfSYhPWdju1m0a3Y9t-wTRtZVFw8YWSJqvWLDxdWqCTI_DuHL-Gw2Os_SvIXMBaffZm6AQjuuaikxAGZq473sFxTRWIFoKqytNqqoua2CprUqvK98bqy0sjbc5E9hf7la4jNgudW5UsL3qxCicK_NQNQhMRvkvFKq0rYH_Eb5pUtk5DQTY1HGpISbkvAqCa8y4dWDt1uRnx0Tx78WHxBA24UJmx4c3SBcpn91XQqiaDMU2Tz_u9RruHs-u5iUk_fTDy_gnqCMO3YjHsF-22zwJdxx1-183byKBvkbAVXeWA
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=Event-Driven+Guaranteed+Cost+Control+Design+for+Nonlinear+Systems+With+Actuator+Faults+via+Reinforcement+Learning+Algorithm&rft.jtitle=IEEE+transactions+on+systems%2C+man%2C+and+cybernetics.+Systems&rft.au=Zhang%2C+Huaguang&rft.au=Liang%2C+Yuling&rft.au=Su%2C+Hanguang&rft.au=Liu%2C+Chong&rft.date=2020-11-01&rft.pub=The+Institute+of+Electrical+and+Electronics+Engineers%2C+Inc.+%28IEEE%29&rft.issn=2168-2216&rft.eissn=2168-2232&rft.volume=50&rft.issue=11&rft.spage=4135&rft_id=info:doi/10.1109%2FTSMC.2019.2946857&rft.externalDBID=NO_FULL_TEXT
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2168-2216&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2168-2216&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2168-2216&client=summon