Functional Nonlinear Model Predictive Control Based on Adaptive Dynamic Programming
This paper presents a functional model predictive control (MPC) approach based on an adaptive dynamic programming (ADP) algorithm with the abilities of handling control constraints and disturbances for the optimal control of nonlinear discrete-time systems. In the proposed ADP-based nonlinear MPC (N...
Uloženo v:
| Vydáno v: | IEEE transactions on cybernetics Ročník 49; číslo 12; s. 4206 - 4218 |
|---|---|
| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
United States
IEEE
01.12.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Témata: | |
| ISSN: | 2168-2267, 2168-2275, 2168-2275 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | This paper presents a functional model predictive control (MPC) approach based on an adaptive dynamic programming (ADP) algorithm with the abilities of handling control constraints and disturbances for the optimal control of nonlinear discrete-time systems. In the proposed ADP-based nonlinear MPC (NMPC) structure, a neural-network-based identification is established first to reconstruct the unknown system dynamics. Then, the actor-critic scheme is adopted with a critic network to estimate the index performance function and an action network to approximate the optimal control input. Meanwhile, as the MPC strategy can effectively determine the current control by solving a finite horizon open-loop optimal control problem, in the proposed algorithm, the infinite horizon is decomposed into a series of finite horizons to obtain the optimal control. In each finite horizon, the finite ADP algorithm solves the optimal control problem subject to the terminal constraint, the control constraint, and the disturbance. The uniform ultimate boundedness of the closed-loop system is verified by the Lyapunov approach. Finally, the ADP-based NMPC is conducted on two different cases and the simulation results demonstrate the quick response and strong robustness of the proposed method. |
|---|---|
| AbstractList | This paper presents a functional model predictive control (MPC) approach based on an adaptive dynamic programming (ADP) algorithm with the abilities of handling control constraints and disturbances for the optimal control of nonlinear discrete-time systems. In the proposed ADP-based nonlinear MPC (NMPC) structure, a neural-network-based identification is established first to reconstruct the unknown system dynamics. Then, the actor-critic scheme is adopted with a critic network to estimate the index performance function and an action network to approximate the optimal control input. Meanwhile, as the MPC strategy can effectively determine the current control by solving a finite horizon open-loop optimal control problem, in the proposed algorithm, the infinite horizon is decomposed into a series of finite horizons to obtain the optimal control. In each finite horizon, the finite ADP algorithm solves the optimal control problem subject to the terminal constraint, the control constraint, and the disturbance. The uniform ultimate boundedness of the closed-loop system is verified by the Lyapunov approach. Finally, the ADP-based NMPC is conducted on two different cases and the simulation results demonstrate the quick response and strong robustness of the proposed method.This paper presents a functional model predictive control (MPC) approach based on an adaptive dynamic programming (ADP) algorithm with the abilities of handling control constraints and disturbances for the optimal control of nonlinear discrete-time systems. In the proposed ADP-based nonlinear MPC (NMPC) structure, a neural-network-based identification is established first to reconstruct the unknown system dynamics. Then, the actor-critic scheme is adopted with a critic network to estimate the index performance function and an action network to approximate the optimal control input. Meanwhile, as the MPC strategy can effectively determine the current control by solving a finite horizon open-loop optimal control problem, in the proposed algorithm, the infinite horizon is decomposed into a series of finite horizons to obtain the optimal control. In each finite horizon, the finite ADP algorithm solves the optimal control problem subject to the terminal constraint, the control constraint, and the disturbance. The uniform ultimate boundedness of the closed-loop system is verified by the Lyapunov approach. Finally, the ADP-based NMPC is conducted on two different cases and the simulation results demonstrate the quick response and strong robustness of the proposed method. This paper presents a functional model predictive control (MPC) approach based on an adaptive dynamic programming (ADP) algorithm with the abilities of handling control constraints and disturbances for the optimal control of nonlinear discrete-time systems. In the proposed ADP-based nonlinear MPC (NMPC) structure, a neural-network-based identification is established first to reconstruct the unknown system dynamics. Then, the actor-critic scheme is adopted with a critic network to estimate the index performance function and an action network to approximate the optimal control input. Meanwhile, as the MPC strategy can effectively determine the current control by solving a finite horizon open-loop optimal control problem, in the proposed algorithm, the infinite horizon is decomposed into a series of finite horizons to obtain the optimal control. In each finite horizon, the finite ADP algorithm solves the optimal control problem subject to the terminal constraint, the control constraint, and the disturbance. The uniform ultimate boundedness of the closed-loop system is verified by the Lyapunov approach. Finally, the ADP-based NMPC is conducted on two different cases and the simulation results demonstrate the quick response and strong robustness of the proposed method. |
| Author | Sun, Changyin Yuan, Xin Dong, Lu He, Haibo Yan, Jun |
| Author_xml | – sequence: 1 givenname: Lu surname: Dong fullname: Dong, Lu email: ldong90@seu.edu.cn organization: School of Automation, Southeast University, Nanjing, China – sequence: 2 givenname: Jun orcidid: 0000-0002-5148-1399 surname: Yan fullname: Yan, Jun email: jun.yan@concordia.ca organization: Department of Electrical, Computer, and Biomedical Engineering, University of Rhode Island, Kingston, RI, USA – sequence: 3 givenname: Xin orcidid: 0000-0002-7472-340X surname: Yuan fullname: Yuan, Xin email: xinyuan@seu.edu.cn organization: School of Automation, Southeast University, Nanjing, China – sequence: 4 givenname: Haibo orcidid: 0000-0002-5247-9370 surname: He fullname: He, Haibo email: haibohe@uri.edu organization: Department of Electrical, Computer, and Biomedical Engineering, University of Rhode Island, Kingston, RI, USA – sequence: 5 givenname: Changyin orcidid: 0000-0001-9269-334X surname: Sun fullname: Sun, Changyin email: cysun@seu.edu.cn organization: School of Automation, Southeast University, Nanjing, China |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/30130246$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9kUtLxDAUhYMovn-ACFJw42bGvJqkSx2f4AvUhauQaW4l0iZj0gr-ezPO6MKF2STkfOfCPWcLrfrgAaE9gseE4Or4afJyOqaYqDFVZaUwWUGblAg1olSWq79vITfQbkpvOB-Vvyq1jjYYJgxTLjbR48Xg694Fb9riLvjWeTCxuA0W2uIhgnVZ_IBiEnwfQ1ucmgS2CL44sWb2rZx9etO5OsPhNZquc_51B601pk2wu7y30fPF-dPkanRzf3k9ObkZ1ayi_Uhy3FijxFQSqKYGJCeUG865gobXQlrJGBeitkTyhgnJiG2mBmdBibIsLdtGR4u5sxjeB0i97lyqoW2NhzAkTXFFVE6AVxk9_IO-hSHmpTNFleRScqUydbCkhmkHVs-i60z81D9xZUAugDqGlCI0una9mcfXR-NaTbCed6Pn3eh5N3rZTXaSP86f4f959hceBwC_vOKswqRkXzwDl5g |
| CODEN | ITCEB8 |
| CitedBy_id | crossref_primary_10_1002_acs_3327 crossref_primary_10_1109_TIV_2022_3167271 crossref_primary_10_1002_rnc_6825 crossref_primary_10_1016_j_oceaneng_2024_116756 crossref_primary_10_1016_j_isatra_2025_08_041 crossref_primary_10_1109_ACCESS_2019_2893243 crossref_primary_10_3390_electronics10091123 crossref_primary_10_1109_TNNLS_2021_3071548 crossref_primary_10_1016_j_isatra_2020_10_009 crossref_primary_10_1109_TSMC_2023_3306338 crossref_primary_10_1109_TSMC_2022_3165586 crossref_primary_10_1109_TASE_2022_3152166 crossref_primary_10_1016_j_jfranklin_2024_107124 crossref_primary_10_1109_TCYB_2019_2926631 crossref_primary_10_1109_TSMC_2023_3254911 crossref_primary_10_1109_TCYB_2022_3233593 crossref_primary_10_1016_j_isatra_2024_02_009 crossref_primary_10_1109_TASE_2020_3019567 crossref_primary_10_1109_TASE_2025_3571535 crossref_primary_10_1109_TCYB_2020_3044595 crossref_primary_10_1002_oca_3287 crossref_primary_10_1109_LCSYS_2024_3408711 crossref_primary_10_1109_TSMC_2024_3486364 crossref_primary_10_1007_s10846_022_01742_w crossref_primary_10_1109_TAES_2022_3197097 crossref_primary_10_1109_TCYB_2020_3021978 crossref_primary_10_1002_oca_2832 crossref_primary_10_1016_j_jclepro_2020_124124 crossref_primary_10_1109_ACCESS_2020_2984311 crossref_primary_10_1109_TSMC_2024_3431453 crossref_primary_10_2514_1_G006915 crossref_primary_10_1109_TNNLS_2022_3172126 crossref_primary_10_1109_TCYB_2019_2906694 crossref_primary_10_1016_j_cja_2024_08_001 crossref_primary_10_1109_TNNLS_2020_3008249 crossref_primary_10_1002_rnc_7114 crossref_primary_10_1109_ACCESS_2021_3051984 crossref_primary_10_1016_j_automatica_2023_111128 crossref_primary_10_1016_j_jfranklin_2021_11_009 crossref_primary_10_1016_j_robot_2025_105128 crossref_primary_10_1016_j_jfranklin_2024_106899 crossref_primary_10_1109_ACCESS_2023_3329685 crossref_primary_10_1002_asjc_2587 crossref_primary_10_1007_s11432_019_2663_y crossref_primary_10_1109_LRA_2025_3530115 crossref_primary_10_1109_TSMC_2023_3331231 crossref_primary_10_3390_s23218995 crossref_primary_10_1109_TSMC_2024_3368026 crossref_primary_10_1002_cta_3370 crossref_primary_10_1109_TSMC_2021_3103013 crossref_primary_10_1109_TSMC_2022_3146284 crossref_primary_10_1016_j_neucom_2021_05_046 crossref_primary_10_1016_j_cose_2024_104186 crossref_primary_10_1109_TIE_2021_3127016 crossref_primary_10_1109_TCYB_2024_3488371 crossref_primary_10_1109_TASE_2022_3217539 crossref_primary_10_1007_s11768_023_00174_7 crossref_primary_10_1109_TNNLS_2021_3138924 crossref_primary_10_1109_TCYB_2023_3262632 crossref_primary_10_1109_TCYB_2021_3121078 crossref_primary_10_1016_j_neunet_2024_106364 crossref_primary_10_1109_TCYB_2021_3064071 |
| Cites_doi | 10.1007/s00521-007-0150-6 10.1109/TSMCB.2012.2203336 10.1109/TAC.2017.2707520 10.1109/72.623201 10.1109/TSMCB.2009.2021950 10.3166/ejc.11.310-334 10.1109/TNN.2007.900227 10.1109/TPWRS.2016.2537984 10.1007/978-3-0348-8407-5_21 10.1016/j.automatica.2010.02.018 10.1109/TNNLS.2012.2227339 10.1109/TCYB.2014.2381604 10.1109/TCYB.2015.2478857 10.1109/TNNLS.2016.2585520 10.1109/TCYB.2015.2445744 10.1016/j.neucom.2011.05.031 10.1109/TSMCB.2008.926614 10.1109/TIE.2016.2542134 10.1109/TNNLS.2012.2196708 10.1109/TNN.2003.813839 10.1109/TCYB.2014.2314612 10.1002/etep.653 10.1109/TNNLS.2013.2281663 10.1109/TSMC.2018.2810117 10.1109/TNNLS.2016.2586303 10.1109/TNNLS.2015.2399020 10.1109/TCYB.2016.2616100 10.1109/TCST.2013.2246866 10.1109/TAC.2010.2049688 10.1109/TSMCB.2012.2216523 10.1109/TNNLS.2016.2541020 10.1109/72.914523 10.1109/TNNLS.2014.2315646 10.1109/TAC.2011.2170109 10.1109/TAC.2004.825980 10.1016/j.neucom.2016.11.041 |
| ContentType | Journal Article |
| Copyright | Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2019 |
| Copyright_xml | – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2019 |
| DBID | 97E RIA RIE AAYXX CITATION NPM 7SC 7SP 7TB 8FD F28 FR3 H8D JQ2 L7M L~C L~D 7X8 |
| DOI | 10.1109/TCYB.2018.2859801 |
| DatabaseName | IEEE All-Society Periodicals Package (ASPP) 2005–Present IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef PubMed Computer and Information Systems Abstracts Electronics & Communications Abstracts Mechanical & Transportation Engineering Abstracts Technology Research Database ANTE: Abstracts in New Technology & Engineering 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 MEDLINE - Academic |
| DatabaseTitle | CrossRef PubMed 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 ANTE: Abstracts in New Technology & Engineering Computer and Information Systems Abstracts Professional MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic PubMed Aerospace Database |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher – sequence: 3 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Sciences (General) |
| EISSN | 2168-2275 |
| EndPage | 4218 |
| ExternalDocumentID | 30130246 10_1109_TCYB_2018_2859801 8439015 |
| Genre | orig-research Journal Article |
| GrantInformation_xml | – fundername: National Natural Science Foundation of China grantid: 61520106009 funderid: 10.13039/501100001809 – fundername: Fundamental Research Funds for the Central Universities funderid: 10.13039/501100012226 – fundername: National Natural Science Foundation grantid: CMMI 1526835 – fundername: Natural Sciences and Engineering Research Council of Canada grantid: RGPIN-2018-06724 funderid: 10.13039/501100000038 |
| GroupedDBID | 0R~ 4.4 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACIWK AENEX AGQYO AGSQL AHBIQ AKJIK AKQYR ALMA_UNASSIGNED_HOLDINGS ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ EBS EJD HZ~ IFIPE IPLJI JAVBF M43 O9- OCL PQQKQ RIA RIE RNS AAYXX CITATION NPM RIG 7SC 7SP 7TB 8FD F28 FR3 H8D JQ2 L7M L~C L~D 7X8 |
| ID | FETCH-LOGICAL-c392t-740fda86b71e9bae74124a4448ef4c67d733466cd174f36731dfba067d86555d3 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 70 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000485687200013&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2168-2267 2168-2275 |
| IngestDate | Sun Sep 28 07:49:00 EDT 2025 Sun Nov 30 03:56:32 EST 2025 Mon Jul 21 06:00:04 EDT 2025 Sat Nov 29 02:02:25 EST 2025 Tue Nov 18 19:50:36 EST 2025 Wed Aug 27 08:36:58 EDT 2025 |
| IsDoiOpenAccess | false |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 12 |
| 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-c392t-740fda86b71e9bae74124a4448ef4c67d733466cd174f36731dfba067d86555d3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ORCID | 0000-0002-7472-340X 0000-0001-9269-334X 0000-0002-5148-1399 0000-0002-5247-9370 |
| OpenAccessLink | https://digitalcommons.uri.edu/ele_facpubs/327 |
| PMID | 30130246 |
| PQID | 2287477488 |
| PQPubID | 85422 |
| PageCount | 13 |
| ParticipantIDs | ieee_primary_8439015 crossref_citationtrail_10_1109_TCYB_2018_2859801 pubmed_primary_30130246 proquest_miscellaneous_2091822649 proquest_journals_2287477488 crossref_primary_10_1109_TCYB_2018_2859801 |
| PublicationCentury | 2000 |
| PublicationDate | 2019-12-01 |
| PublicationDateYYYYMMDD | 2019-12-01 |
| PublicationDate_xml | – month: 12 year: 2019 text: 2019-12-01 day: 01 |
| PublicationDecade | 2010 |
| PublicationPlace | United States |
| PublicationPlace_xml | – name: United States – name: Piscataway |
| PublicationTitle | IEEE transactions on cybernetics |
| PublicationTitleAbbrev | TCYB |
| PublicationTitleAlternate | IEEE Trans Cybern |
| PublicationYear | 2019 |
| 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 ref32 findeisen (ref10) 2002 ref39 ref17 ref38 ref18 qin (ref2) 1997; 93 liu (ref16) 2013; 43 wang (ref1) 2009 rawlings (ref5) 2009 chen (ref9) 2013 liu (ref22) 2014; 25 ref24 ref23 ref26 ref25 ref41 ref44 ref21 ref43 ref28 ref27 loop (ref42) 2005 bellman (ref19) 1957 ref29 ref8 ref7 werbos (ref20) 1977; 22 ref4 ref3 ref6 ref40 |
| References_xml | – ident: ref44 doi: 10.1007/s00521-007-0150-6 – ident: ref27 doi: 10.1109/TSMCB.2012.2203336 – ident: ref35 doi: 10.1109/TAC.2017.2707520 – ident: ref15 doi: 10.1109/72.623201 – ident: ref25 doi: 10.1109/TSMCB.2009.2021950 – ident: ref13 doi: 10.3166/ejc.11.310-334 – volume: 22 start-page: 25 year: 1977 ident: ref20 article-title: Advanced forecasting methods for global crisis warning and models of intelligence publication-title: Gen Syst Yearbook – ident: ref41 doi: 10.1109/TNN.2007.900227 – ident: ref36 doi: 10.1109/TPWRS.2016.2537984 – ident: ref3 doi: 10.1007/978-3-0348-8407-5_21 – ident: ref38 doi: 10.1016/j.automatica.2010.02.018 – ident: ref37 doi: 10.1109/TNNLS.2012.2227339 – ident: ref12 doi: 10.1109/TCYB.2014.2381604 – ident: ref14 doi: 10.1109/TCYB.2015.2478857 – ident: ref29 doi: 10.1109/TNNLS.2016.2585520 – ident: ref11 doi: 10.1109/TCYB.2015.2445744 – year: 2009 ident: ref5 publication-title: Model Predictive Control Theory and Design – ident: ref31 doi: 10.1016/j.neucom.2011.05.031 – year: 2013 ident: ref9 publication-title: Model Predictive Control – ident: ref23 doi: 10.1109/TSMCB.2008.926614 – ident: ref17 doi: 10.1109/TIE.2016.2542134 – ident: ref39 doi: 10.1109/TNNLS.2012.2196708 – ident: ref26 doi: 10.1109/TNN.2003.813839 – ident: ref24 doi: 10.1109/TCYB.2014.2314612 – ident: ref8 doi: 10.1002/etep.653 – volume: 25 start-page: 621 year: 2014 ident: ref22 article-title: Policy iteration adaptive dynamic programming algorithm for discrete-time nonlinear systems publication-title: IEEE Trans Neural Netw Learn Syst doi: 10.1109/TNNLS.2013.2281663 – ident: ref32 doi: 10.1109/TSMC.2018.2810117 – volume: 93 start-page: 232 year: 1997 ident: ref2 article-title: An overview of industrial model predictive control technology publication-title: Control Eng Pract – ident: ref34 doi: 10.1109/TNNLS.2016.2586303 – ident: ref28 doi: 10.1109/TNNLS.2015.2399020 – ident: ref4 doi: 10.1109/TCYB.2016.2616100 – year: 2009 ident: ref1 publication-title: Model Predictive Control System Design and Implementation Using MATLAB – ident: ref30 doi: 10.1109/TCST.2013.2246866 – year: 1957 ident: ref19 publication-title: Dynamic Programming – ident: ref43 doi: 10.1109/TAC.2010.2049688 – volume: 43 start-page: 779 year: 2013 ident: ref16 article-title: Finite-approximation-error-based optimal control approach for discrete-time nonlinear systems publication-title: IEEE Trans Cybern doi: 10.1109/TSMCB.2012.2216523 – ident: ref18 doi: 10.1109/TNNLS.2016.2541020 – ident: ref21 doi: 10.1109/72.914523 – start-page: 1 year: 2002 ident: ref10 article-title: An introduction to nonlinear model predictive control publication-title: Proc 21st Benelux Meeting Syst Control – ident: ref40 doi: 10.1109/TNNLS.2014.2315646 – year: 2005 ident: ref42 article-title: Estimating regions of asymptotic stability of nonlinear systems with applications to power electronics systems – ident: ref7 doi: 10.1109/TAC.2011.2170109 – ident: ref6 doi: 10.1109/TAC.2004.825980 – ident: ref33 doi: 10.1016/j.neucom.2016.11.041 |
| SSID | ssj0000816898 |
| Score | 2.49387 |
| Snippet | This paper presents a functional model predictive control (MPC) approach based on an adaptive dynamic programming (ADP) algorithm with the abilities of... |
| SourceID | proquest pubmed crossref ieee |
| SourceType | Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 4206 |
| SubjectTerms | Adaptive algorithms Adaptive control Adaptive dynamic programming (ADP) Algorithms Artificial neural networks Computer simulation Discrete time systems Dynamic programming Economic models Feedback control Heuristic algorithms Horizon Mathematical model Neural networks neural networks (NNs) Nonlinear control nonlinear model predictive control (NMPC) Nonlinear systems Optimal control Predictive control System dynamics Terminal constraints |
| Title | Functional Nonlinear Model Predictive Control Based on Adaptive Dynamic Programming |
| URI | https://ieeexplore.ieee.org/document/8439015 https://www.ncbi.nlm.nih.gov/pubmed/30130246 https://www.proquest.com/docview/2287477488 https://www.proquest.com/docview/2091822649 |
| Volume | 49 |
| WOSCitedRecordID | wos000485687200013&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: 2168-2275 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000816898 issn: 2168-2267 databaseCode: RIE dateStart: 20130101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LS8QwEB5URLz4ftQXETyo2LWP2KRHXV08LYIK66k0TQqCdmUf_n5nkmzxoIK3QtI2ZCaZL5nHB3CSIKYWiZIhNzkP0QSoMC-lCmOuMlMjAqlrZckmRL8vB4P8YQ4u2lwYY4wNPjMderS-fD2spnRVdim5PaHPw7wQwuVqtfcplkDCUt8m-BAiqhDeiRlH-eVT9-WG4rhkhwq24a68DEupddoR8v1mkSzFyu9o01qd3ur_xrsGKx5dsmunDuswZ5oNWPfrd8xOfZHps0147KFBc_eArO_KZZQjRsxob-xhRN4b2gdZ10Wysxs0dpoNG3atyw_bcuuo7LGzDfB6RxO4Bc-9u6fufegJFsIKYdEkFDyqdSkzJWKTq9IIoqIuOZ7YTM2rTGiRpjzLKo3HljrNRBrrWpVo3zSls17pdBsWmmFjdoHxRFNp0jpWRvHECBlHlZKJQXiQVij0AKLZJBeVrz5OJBhvhT2FRHlBIipIRIUXUQDn7SsfrvTGX503af7bjn7qAziYSbLwi3NcJFTjH2GvlAEct824rMhXUjZmOMU-iKMkJRnnAew4DWi_PVOcvZ__uQ_LOLLcxbwcwMJkNDWHsFh9Tl7HoyPU3YE8srr7BZuw5hk |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LT9wwEB4BrYBLy6OloTyMxKFUzZKHiZ0jLKxA0BVSFwlOURw7EtKSRfvo72fG9kY9UCRukTxJLI_t-cYzng_gMEFMLRIlQ25yHqIJUGFeShXGXGWmRgRS18qSTYh-X97f57cL8Ku9C2OMsclnpkOPNpavR9WMjsqOJbce-iJ8OOE8id1trfZExVJIWPLbBB9CxBXChzHjKD8edB_OKJNLdqhkG-7Lq7Cc2rAdYd9_bJIlWfk_3rR2p_f5fT1eg08eX7JTNyHWYcE0G7DuV_CE_fBlpo824U8PTZo7CWR9VzCjHDPiRhuy2zHFb2gnZF2Xy87O0NxpNmrYqS6fbcu5I7NHYZvi9YRG8Avc9S4G3cvQUyyEFQKjaSh4VOtSZkrEJlelEURGXXL02UzNq0xokaY8yyqNjkudZiKNda1KtHCaLrSe6PQrLDWjxnwDxhNNxUnrWBnFEyNkHFVKJgYBQlqh2gOI5oNcVL7-ONFgDAvrh0R5QSoqSEWFV1EAP9tXnl3xjbeEN2n8W0E_9AHszDVZ-OU5KRKq8o_AV8oADtpmXFgULSkbM5qhDCIpSdeM8wC23Axovz2fONuv_3MfVi4Hv2-Km6v-9XdYxV7mLgNmB5am45nZhY_V3-njZLxnZ_ALT2joeA |
| 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=Functional+Nonlinear+Model+Predictive+Control+Based+on+Adaptive+Dynamic+Programming&rft.jtitle=IEEE+transactions+on+cybernetics&rft.au=Dong%2C+Lu&rft.au=Yan%2C+Jun&rft.au=Yuan%2C+Xin&rft.au=He%2C+Haibo&rft.date=2019-12-01&rft.pub=IEEE&rft.issn=2168-2267&rft.volume=49&rft.issue=12&rft.spage=4206&rft.epage=4218&rft_id=info:doi/10.1109%2FTCYB.2018.2859801&rft_id=info%3Apmid%2F30130246&rft.externalDocID=8439015 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2168-2267&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2168-2267&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2168-2267&client=summon |