Neural Networks Enhanced Optimal Admittance Control of Robot-Environment Interaction Using Reinforcement Learning

In this paper, an adaptive admittance control scheme is developed for robots to interact with time-varying environments. Admittance control is adopted to achieve a compliant physical robot-environment interaction, and the uncertain environment with time-varying dynamics is defined as a linear system...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	IEEE transaction on neural networks and learning systems Ročník 33; číslo 9; s. 4551 - 4561
Hlavní autori:	Peng, Guangzhu, Chen, C. L. Philip, Yang, Chenguang
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.09.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Adaptive control Admittance admittance control Artificial neural networks Control systems Cost function Electrical impedance Impedance Learning Learning systems Manipulators Neural networks neural networks (NNs) reinforcement learning (RL) Robot control robot–environment interaction Tracking Trajectory Uncertainty
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	In this paper, an adaptive admittance control scheme is developed for robots to interact with time-varying environments. Admittance control is adopted to achieve a compliant physical robot-environment interaction, and the uncertain environment with time-varying dynamics is defined as a linear system. A critic learning method is used to obtain the desired admittance parameters based on the cost function composed of interaction force and trajectory tracking without the knowledge of the environmental dynamics. To deal with dynamic uncertainties in the control system, a neural-network (NN)-based adaptive controller with a dynamic learning framework is developed to guarantee the trajectory tracking performance. Experiments are conducted and the results have verified the effectiveness of the proposed method.
AbstractList	In this paper, an adaptive admittance control scheme is developed for robots to interact with time-varying environments. Admittance control is adopted to achieve a compliant physical robot-environment interaction, and the uncertain environment with time-varying dynamics is defined as a linear system. A critic learning method is used to obtain the desired admittance parameters based on the cost function composed of interaction force and trajectory tracking without the knowledge of the environmental dynamics. To deal with dynamic uncertainties in the control system, a neural-network (NN)-based adaptive controller with a dynamic learning framework is developed to guarantee the trajectory tracking performance. Experiments are conducted and the results have verified the effectiveness of the proposed method. In this paper, an adaptive admittance control scheme is developed for robots to interact with time-varying environments. Admittance control is adopted to achieve a compliant physical robot-environment interaction, and the uncertain environment with time-varying dynamics is defined as a linear system. A critic learning method is used to obtain the desired admittance parameters based on the cost function composed of interaction force and trajectory tracking without the knowledge of the environmental dynamics. To deal with dynamic uncertainties in the control system, a neural-network (NN)-based adaptive controller with a dynamic learning framework is developed to guarantee the trajectory tracking performance. Experiments are conducted and the results have verified the effectiveness of the proposed method.In this paper, an adaptive admittance control scheme is developed for robots to interact with time-varying environments. Admittance control is adopted to achieve a compliant physical robot-environment interaction, and the uncertain environment with time-varying dynamics is defined as a linear system. A critic learning method is used to obtain the desired admittance parameters based on the cost function composed of interaction force and trajectory tracking without the knowledge of the environmental dynamics. To deal with dynamic uncertainties in the control system, a neural-network (NN)-based adaptive controller with a dynamic learning framework is developed to guarantee the trajectory tracking performance. Experiments are conducted and the results have verified the effectiveness of the proposed method.
Author	Chen, C. L. Philip Peng, Guangzhu Yang, Chenguang
Author_xml	– sequence: 1 givenname: Guangzhu orcidid: 0000-0003-3950-0451 surname: Peng fullname: Peng, Guangzhu email: gz.peng@qq.com organization: Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Macau, China – 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 – sequence: 3 givenname: Chenguang orcidid: 0000-0001-5255-5559 surname: Yang fullname: Yang, Chenguang email: cyang@ieee.org organization: School of Automation Science and Engineering, South China University of Technology, Guangzhou, China
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/33651696$$D View this record in MEDLINE/PubMed
BookMark	eNp9kU9v1DAQxS1UREvpFwAJWeLCJYv_xI59rFYLVFptpdJK3KzEmYBLYm9tB9RvX29320MP-GJr5vdG4_feoiMfPCD0npIFpUR_ud5s1j8WjDC64EQ0WqhX6IRRySrGlTp6fjc_j9FZSrekHEmErPUbdMy5FFRqeYLuNjDHdsQbyP9C_JPwyv9uvYUeX26zm0rnvJ9czrsaXgafYxhxGPBV6EKuVv6vi8FP4DO-8Blia7MLHt8k53_hK3B-CNHCY38NbfSl_A69HtoxwdnhPkU3X1fXy-_V-vLbxfJ8XVkuaK607sDyjgDp-_KjtqO6ByHoUEvb1Qo4VYw2lFjR9PUgQPW6E6qjg4KaUAL8FH3ez93GcDdDymZyycI4th7CnAyrtWSCcckK-ukFehvm6Mt2hjVEyZrVTBXq44Gauwl6s43Fn3hvnswsgNoDNoaUIgzGutzuDMmxdaOhxOyiM4_RmV105hBdkbIX0qfp_xV92IscADwLNJcNIYI_AOYtpQ8
CODEN	ITNNAL
CitedBy_id	crossref_primary_10_3390_math13132204 crossref_primary_10_3390_s22072523 crossref_primary_10_1007_s41315_024_00362_x crossref_primary_10_1155_2022_4332766 crossref_primary_10_1109_TMECH_2022_3221931 crossref_primary_10_1177_01423312231203270 crossref_primary_10_1002_rnc_6493 crossref_primary_10_1016_j_isatra_2024_08_013 crossref_primary_10_1016_j_neunet_2024_106810 crossref_primary_10_1109_TFUZZ_2023_3247693 crossref_primary_10_1109_ACCESS_2023_3347416 crossref_primary_10_1155_2021_4281006 crossref_primary_10_1109_LRA_2022_3147245 crossref_primary_10_1007_s12555_023_0358_y crossref_primary_10_1109_TNNLS_2021_3131261 crossref_primary_10_1007_s12555_021_0430_4 crossref_primary_10_1016_j_eswa_2024_125354 crossref_primary_10_1109_TSMC_2025_3577296 crossref_primary_10_1108_IR_04_2024_0176 crossref_primary_10_1109_TCSII_2021_3128958 crossref_primary_10_1007_s12555_024_0165_0 crossref_primary_10_1016_j_isatra_2023_02_021 crossref_primary_10_1109_TCSI_2023_3327254 crossref_primary_10_1109_TIM_2024_3379048 crossref_primary_10_1109_TNNLS_2022_3165941 crossref_primary_10_3390_act14070323 crossref_primary_10_1007_s11227_025_07270_2 crossref_primary_10_1016_j_neucom_2021_06_089 crossref_primary_10_1007_s11431_022_2205_9 crossref_primary_10_3390_machines13030207 crossref_primary_10_1177_01423312241286322 crossref_primary_10_1109_TASE_2022_3142565 crossref_primary_10_3389_fnbot_2022_1068706 crossref_primary_10_1109_TSMC_2024_3430265 crossref_primary_10_3390_act13090354 crossref_primary_10_1007_s40789_022_00510_3 crossref_primary_10_1108_IR_01_2025_0033 crossref_primary_10_3390_en16041677 crossref_primary_10_1186_s10033_024_01113_6 crossref_primary_10_1007_s41315_025_00494_8 crossref_primary_10_1016_j_ast_2022_108060 crossref_primary_10_1016_j_engappai_2025_110678
Cites_doi	10.1142/S0218127409023640 10.1109/TNNLS.2018.2806347 10.1109/TCYB.2018.2808321 10.1109/3477.485879 10.1109/TCYB.2017.2726039 10.7551/mitpress/4939.003.0007 10.1109/TNN.2005.860843 10.1109/JAS.2020.1003417 10.1109/MCS.2012.2214134 10.1109/TSMC.1981.4308708 10.1109/TNNLS.2018.2852711 10.1109/TSMCB.2008.924139 10.1109/TNNLS.2018.2876352 10.1177/0278364905056347 10.1109/TIE.2019.2893847 10.1109/TNNLS.2014.2378812 10.1080/00207179.2013.827799 10.1109/CDC.1984.272176 10.1109/TFUZZ.2015.2396075 10.1109/TMECH.2020.2987963 10.1016/j.neunet.2009.06.021 10.1016/0005-1098(96)85559-2 10.1109/TRO.2011.2158251 10.1007/978-1-4020-6272-8_6 10.1109/TCST.2013.2286194 10.1109/TSMCB.2009.2026289 10.1177/0278364911402527 10.1109/TIE.2019.2912781 10.1109/TRO.2016.2597322 10.1109/TIE.2019.2950853 10.1109/TII.2016.2612646 10.1109/CMPSAC.1979.762539 10.1016/s0094-114x(02)00045-9 10.1109/TSMC.2015.2429555 10.1115/1.3140702 10.1109/TNNLS.2018.2828813 10.1109/TNNLS.2018.2866622 10.1007/978-981-10-0830-6 10.1049/iet-cta:20070096 10.1109/56.804 10.1080/00207179.2011.642309 10.1109/TNNLS.2018.2886023 10.1109/MCAS.2009.933854 10.1109/TCST.2016.2523901 10.1109/TCYB.2014.2329931 10.1109/TCYB.2016.2573837 10.1109/ROBOT.1994.351417 10.1109/TNN.2010.2076370 10.1109/TNNLS.2017.2665581 10.1109/TNNLS.2017.2716952 10.1109/TMECH.2014.2375638 10.1109/TIE.2019.2947844
ContentType	Journal Article
Copyright	Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022
Copyright_xml	– notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022
DBID	97E RIA RIE AAYXX CITATION NPM 7QF 7QO 7QP 7QQ 7QR 7SC 7SE 7SP 7SR 7TA 7TB 7TK 7U5 8BQ 8FD F28 FR3 H8D JG9 JQ2 KR7 L7M L~C L~D P64 7X8
DOI	10.1109/TNNLS.2021.3057958
DatabaseName	IEEE Xplore (IEEE) 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	PubMed MEDLINE - Academic 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: 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	4561
ExternalDocumentID	33651696 10_1109_TNNLS_2021_3057958 9367005
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: 0119/2018/A3 – fundername: Multiyear Research Grants of University of Macau – fundername: National Natural Science Foundation of China grantid: 61702195; 61751202; U1813203; U1801262; 61751205; U20A20200; 61861136009 funderid: 10.13039/501100001809 – fundername: Science and Technology Major Project of Guangzhou grantid: 202007030006
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-99bec3b0e0dd238ab19de551f46cb48e31821710c57d4f5e8d9b58b1f8e4010e3
IEDL.DBID	RIE
ISICitedReferencesCount	64
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000732268100001&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	Wed Oct 01 13:52:49 EDT 2025 Sun Jun 29 15:37:49 EDT 2025 Thu Jan 02 22:56:27 EST 2025 Sat Nov 29 01:40:11 EST 2025 Tue Nov 18 21:45:11 EST 2025 Wed Aug 27 02:29:21 EDT 2025
IsPeerReviewed	false
IsScholarly	true
Issue	9
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-99bec3b0e0dd238ab19de551f46cb48e31821710c57d4f5e8d9b58b1f8e4010e3
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ORCID	0000-0003-3950-0451 0000-0001-5451-7230 0000-0001-5255-5559
PMID	33651696
PQID	2708642428
PQPubID	85436
PageCount	11
ParticipantIDs	crossref_citationtrail_10_1109_TNNLS_2021_3057958 proquest_miscellaneous_2496252362 ieee_primary_9367005 proquest_journals_2708642428 crossref_primary_10_1109_TNNLS_2021_3057958 pubmed_primary_33651696
PublicationCentury	2000
PublicationDate	2022-09-01
PublicationDateYYYYMMDD	2022-09-01
PublicationDate_xml	– month: 09 year: 2022 text: 2022-09-01 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	2022
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 ref12 ref56 ref15 ref14 ref53 ref11 ref55 ref10 ref54 ref17 ref16 ref19 ref18 Lee (ref46) 1998; 19 ref51 ref50 ref45 ref48 ref42 ref41 ref44 ref43 ref8 ref7 ref9 ref4 ref3 ref6 ref5 ref40 Bertsekas (ref13) 2011; 2 ref35 ref34 ref37 ref36 ref31 Landelius (ref47) 1997 ref30 ref33 ref32 Åström (ref49) 2013 ref2 ref1 ref39 ref38 ref24 ref23 ref26 ref25 ref20 ref22 Slotine (ref52) 1987; 2 ref21 ref28 ref27 ref29
References_xml	– ident: ref43 doi: 10.1142/S0218127409023640 – ident: ref15 doi: 10.1109/TNNLS.2018.2806347 – ident: ref35 doi: 10.1109/TCYB.2018.2808321 – ident: ref23 doi: 10.1109/3477.485879 – ident: ref28 doi: 10.1109/TCYB.2017.2726039 – ident: ref12 doi: 10.7551/mitpress/4939.003.0007 – ident: ref42 doi: 10.1109/TNN.2005.860843 – ident: ref29 doi: 10.1109/JAS.2020.1003417 – ident: ref19 doi: 10.1109/MCS.2012.2214134 – ident: ref4 doi: 10.1109/TSMC.1981.4308708 – ident: ref31 doi: 10.1109/TNNLS.2018.2852711 – volume-title: Adaptive Control year: 2013 ident: ref49 – ident: ref17 doi: 10.1109/TSMCB.2008.924139 – ident: ref32 doi: 10.1109/TNNLS.2018.2876352 – ident: ref11 doi: 10.1177/0278364905056347 – ident: ref26 doi: 10.1109/TIE.2019.2893847 – ident: ref48 doi: 10.1109/TNNLS.2014.2378812 – ident: ref24 doi: 10.1080/00207179.2013.827799 – ident: ref22 doi: 10.1109/CDC.1984.272176 – ident: ref30 doi: 10.1109/TFUZZ.2015.2396075 – ident: ref27 doi: 10.1109/TMECH.2020.2987963 – volume: 2 volume-title: Dynamic Programming and Optimal Control year: 2011 ident: ref13 – ident: ref16 doi: 10.1016/j.neunet.2009.06.021 – ident: ref51 doi: 10.1016/0005-1098(96)85559-2 – ident: ref9 doi: 10.1109/TRO.2011.2158251 – ident: ref25 doi: 10.1007/978-1-4020-6272-8_6 – ident: ref8 doi: 10.1109/TCST.2013.2286194 – ident: ref20 doi: 10.1109/TSMCB.2009.2026289 – ident: ref21 doi: 10.1177/0278364911402527 – ident: ref33 doi: 10.1109/TIE.2019.2912781 – ident: ref7 doi: 10.1109/TRO.2016.2597322 – ident: ref44 doi: 10.1109/TIE.2019.2950853 – volume: 19 volume-title: Adaptive Neural Network Control of Robotic Manipulators year: 1998 ident: ref46 – ident: ref36 doi: 10.1109/TII.2016.2612646 – ident: ref2 doi: 10.1109/CMPSAC.1979.762539 – ident: ref45 doi: 10.1016/s0094-114x(02)00045-9 – ident: ref57 doi: 10.1109/TSMC.2015.2429555 – ident: ref3 doi: 10.1115/1.3140702 – ident: ref55 doi: 10.1109/TNNLS.2018.2828813 – year: 1997 ident: ref47 article-title: Reinforcement learning and distributed local model synthesis – ident: ref40 doi: 10.1109/TNNLS.2018.2866622 – ident: ref54 doi: 10.1007/978-981-10-0830-6 – ident: ref50 doi: 10.1049/iet-cta:20070096 – ident: ref53 doi: 10.1109/56.804 – ident: ref6 doi: 10.1080/00207179.2011.642309 – ident: ref34 doi: 10.1109/TNNLS.2018.2886023 – ident: ref18 doi: 10.1109/MCAS.2009.933854 – ident: ref5 doi: 10.1109/TCST.2016.2523901 – ident: ref38 doi: 10.1109/TCYB.2014.2329931 – volume: 2 start-page: 86 issue: 2 year: 1987 ident: ref52 article-title: A rule-based inverse kinematics algorithm for redundant manipulators publication-title: Int. J. Robot. Autom. – ident: ref56 doi: 10.1109/TCYB.2016.2573837 – ident: ref10 doi: 10.1109/ROBOT.1994.351417 – ident: ref14 doi: 10.1109/TNN.2010.2076370 – ident: ref37 doi: 10.1109/TNNLS.2017.2665581 – ident: ref39 doi: 10.1109/TNNLS.2017.2716952 – ident: ref1 doi: 10.1109/TMECH.2014.2375638 – ident: ref41 doi: 10.1109/TIE.2019.2947844
SSID	ssj0000605649
Score	2.596947
Snippet	In this paper, an adaptive admittance control scheme is developed for robots to interact with time-varying environments. Admittance control is adopted to...
SourceID	proquest pubmed crossref ieee
SourceType	Aggregation Database Index Database Enrichment Source Publisher
StartPage	4551
SubjectTerms	Adaptive control Admittance admittance control Artificial neural networks Control systems Cost function Electrical impedance Impedance Learning Learning systems Manipulators Neural networks neural networks (NNs) reinforcement learning (RL) Robot control robot–environment interaction Tracking Trajectory Uncertainty
Title	Neural Networks Enhanced Optimal Admittance Control of Robot-Environment Interaction Using Reinforcement Learning
URI	https://ieeexplore.ieee.org/document/9367005 https://www.ncbi.nlm.nih.gov/pubmed/33651696 https://www.proquest.com/docview/2708642428 https://www.proquest.com/docview/2496252362
Volume	33
WOSCitedRecordID	wos000732268100001&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: 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/eLvHCXMwlV1LS-UwFD6ouJiNzxm9vsjA7DTaJm2aLEWuuJAqjiN3V9okVUFb9fb6-z1JHyI4grvSniSF7yTnkfMA-MOKWBjUvKlkKqKRFJIWCDS1THBjFZfCB4_fnCdpKicTdTkHB0MujLXWB5_ZQ_fo7_JNrWfOVXakXLUxV7B0PklEm6s1-FMC1MuF13ZZKBhlPJn0OTKBOrpO0_O_aA2y8JC79MvY9enjXLhbIvFBJPkeK_9XN73YOV3-3g-vwFKnXpLjlh9WYc5Wa7Dct24g3U5eh2dXlAMJ0zYKfErG1Z2PBSAXeIY8uinM433TuHfkpA1nJ3VJruqibuj4PTuOeJdimx1BfPwBubK-Gqv2jkfSFXC9_Qn_TsfXJ2e0675ANY_DhiqF8PIisIExKNdzhNBY1K_KSOgiks53iuZMGOg4MVEZW2lUEcsiLKVFmy2w_BcsVHVlN4GYoGS5ZFonOZ4RhZKclygH81wpXCLhIwh7ADLdlSZ3HTIeMm-iBCrz-GUOv6zDbwT7w5intjDHl9TrDp2BsgNmBDs9zlm3d6cZS9DMi1B1wVG_h8-469xVSl7ZeoY0kULDkaH0H8FGyx_D3D1bbX2-5jb8YC6Fwsep7cBC8zKzu7CoX5v76csesvZE7nnWfgPpWPKP
linkProvider	IEEE
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Lb9QwEB5VBQkuFCiULS0YiRu4dWzHsY-o2qqIJaCyoL1Fie1AJZrQbpbfz9h5oEqAxC1Kxnakb-x5eB4AL3mVKoeaN9XcSCq10rRCoKnnSjhvhFYxePzLIstzvVqZj1vwesqF8d7H4DN_FB7jXb5r7Sa4yo5NqDYWCpbeSqXkrM_WmjwqDDVzFfVdnihOuchWY5YMM8fLPF98QnuQJ0ciJGCmoVOfECrcE6kbQil2Wfm7whkFz-nO__3yfbg3KJjkTc8RD2DLNw9hZ2zeQIa9vAtXoSwHEuZ9HPiazJtvMRqAfMBT5DJM4S4vui68Iyd9QDtpa3LeVm1H57_z40h0Kvb5ESRGIJBzH-ux2uh6JEMJ16-P4PPpfHlyRof-C9SKNOmoMQiwqJhnzqFkLxFE51HDqqWyldTBe4oGTcJsmjlZp147U6W6Smrt0WpjXjyG7aZt_BMgjtW81NzarMRTojJaiBolYVkag0tkYgbJCEBhh-LkoUfG9yIaKcwUEb8i4FcM-M3g1TTmR1-a45_UuwGdiXIAZgYHI87FsHvXBc_Q0JOovOCoF9Nn3HfhMqVsfLtBGmnQdOQo_2ew1_PHNPfIVvt_XvM53Dlbvl8Ui7f5u6dwl4eEihi1dgDb3fXGH8Jt-7O7WF8_iwz-C_Fj9O4
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+Networks+Enhanced+Optimal+Admittance+Control+of+Robot-Environment+Interaction+Using+Reinforcement+Learning&rft.jtitle=IEEE+transaction+on+neural+networks+and+learning+systems&rft.au=Peng%2C+Guangzhu&rft.au=Chen%2C+C.+L.+Philip&rft.au=Yang%2C+Chenguang&rft.date=2022-09-01&rft.pub=IEEE&rft.issn=2162-237X&rft.volume=33&rft.issue=9&rft.spage=4551&rft.epage=4561&rft_id=info:doi/10.1109%2FTNNLS.2021.3057958&rft_id=info%3Apmid%2F33651696&rft.externalDocID=9367005
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