Combining hybrid metaheuristic algorithms and reinforcement learning to improve the optimal control of nonlinear continuous-time systems with input constraints
This paper proposes an innovative method for achieving optimal tracking control in nonlinear continuous-time systems with input constraints. The method combines reinforcement learning and hybrid metaheuristics to enhance the controller’s performance. Specifically, a hybrid metaheuristic algorithm is...
Uloženo v:
| Vydáno v: | Computers & electrical engineering Ročník 116; s. 109179 |
|---|---|
| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier Ltd
01.05.2024
|
| Témata: | |
| ISSN: | 0045-7906, 1879-0755 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | This paper proposes an innovative method for achieving optimal tracking control in nonlinear continuous-time systems with input constraints. The method combines reinforcement learning and hybrid metaheuristics to enhance the controller’s performance. Specifically, a hybrid metaheuristic algorithm is employed to optimize the hyperparameters of a critic neural network, which serves as the system’s controller. The proposed approach is evaluated through extensive simulation studies on a nonlinear system with input constraints. Results demonstrate its superiority over traditional control techniques in terms of accuracy, timeliness, and stability. Notably, the approach effectively eliminates overshoot and steady-state error while providing precise and prompt tracking and showcasing remarkable robustness against model uncertainties. By synergistically integrating reinforcement learning and hybrid metaheuristics, this approach represents a significant advancement in enhancing the control performance of complex nonlinear systems. The simulation studies confirm superiority of the proposed approach over existing techniques, offering a promising solution for achieving optimal tracking control in nonlinear systems with input constraints. This approach holds potential for a wide range of applications, including robotics, aerospace, and manufacturing, where precise and prompt tracking control is critical. |
|---|---|
| AbstractList | This paper proposes an innovative method for achieving optimal tracking control in nonlinear continuous-time systems with input constraints. The method combines reinforcement learning and hybrid metaheuristics to enhance the controller’s performance. Specifically, a hybrid metaheuristic algorithm is employed to optimize the hyperparameters of a critic neural network, which serves as the system’s controller. The proposed approach is evaluated through extensive simulation studies on a nonlinear system with input constraints. Results demonstrate its superiority over traditional control techniques in terms of accuracy, timeliness, and stability. Notably, the approach effectively eliminates overshoot and steady-state error while providing precise and prompt tracking and showcasing remarkable robustness against model uncertainties. By synergistically integrating reinforcement learning and hybrid metaheuristics, this approach represents a significant advancement in enhancing the control performance of complex nonlinear systems. The simulation studies confirm superiority of the proposed approach over existing techniques, offering a promising solution for achieving optimal tracking control in nonlinear systems with input constraints. This approach holds potential for a wide range of applications, including robotics, aerospace, and manufacturing, where precise and prompt tracking control is critical. |
| ArticleNumber | 109179 |
| Author | Solaymani Fard, Omid Khalili Amirabadi, Roya |
| Author_xml | – sequence: 1 givenname: Roya surname: Khalili Amirabadi fullname: Khalili Amirabadi, Roya email: Roya.khalili.a@gmail.com – sequence: 2 givenname: Omid surname: Solaymani Fard fullname: Solaymani Fard, Omid email: soleimani@um.ac.ir, omidsfard@gmail.com |
| BookMark | eNqNkM1uGyEUhVGVSnWSvgN9gHFhfmBmVUVW2lSKlE2yRgxc7GvNwAhwIj9NXrU47iLKKqsrrs75LudckgsfPBDyg7M1Z1z83K9NmBeYwIDfrmtWt2U_cDl8ISvey6FisusuyIqxtqvkwMQ3cpnSnpW34P2KvG7CPKJHv6W74xjR0hmy3sEhYspoqJ62IWLezYlqb2kE9C5EAzP4TCfQ8c2aA8V5ieEZaN4BDUvGWU_UBJ9jmGhwtHx7Ql_0b0v0h3BIVVEBTceUoeBfyhWKfjnkkyTlqNHndE2-Oj0l-P5_XpGn37ePm7vq_uHP383NfWWamueKt6aRgrfgdNtb0TfSjpo5oUdbN87IYbRN4_pO9NqIUY6ytmDaRtTQOl13dXNFhjPXxJBSBKeWWDLEo-JMnZpWe_WuaXVqWp2bLt5fH7wGs854Sq9x-hRhcyZAifiMEFUyCN6AxQgmKxvwE5R_tJCrzQ |
| CitedBy_id | crossref_primary_10_1088_2631_8695_ae0257 crossref_primary_10_1007_s11071_025_11513_5 |
| Cites_doi | 10.1109/MCS.2012.2214134 10.1016/j.ifacol.2018.11.115 10.1007/s11768-022-00081-3 10.1016/j.ins.2020.11.057 10.1007/s11063-020-10220-z 10.1049/cth2.12037 10.1016/j.cie.2019.106040 10.1109/FUZZ-IEEE.2012.6251315 10.1007/s10589-010-9329-3 10.1109/TSMC.2022.3220028 10.1109/TNNLS.2015.2441712 10.15388/NA.2016.3.7 10.4249/scholarpedia.2928 10.1016/j.ins.2022.05.048 10.1049/iet-cta.2015.0769 10.1109/DDCLS52934.2021.9455473 10.1016/j.neucom.2018.07.098 10.1017/S0263574720000314 |
| ContentType | Journal Article |
| Copyright | 2024 Elsevier Ltd |
| Copyright_xml | – notice: 2024 Elsevier Ltd |
| DBID | AAYXX CITATION |
| DOI | 10.1016/j.compeleceng.2024.109179 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 1879-0755 |
| ExternalDocumentID | 10_1016_j_compeleceng_2024_109179 S0045790624001071 |
| GroupedDBID | --K --M .DC .~1 0R~ 1B1 1~. 1~5 29F 4.4 457 4G. 5GY 5VS 7-5 71M 8P~ 9JN AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AAXUO AAYFN ABBOA ABEFU ABFNM ABJNI ABMAC ABXDB ACDAQ ACGFO ACGFS ACNNM ACRLP ACZNC ADBBV ADEZE ADJOM ADMUD ADTZH AEBSH AECPX AEKER AENEX AFFNX AFKWA AFTJW AGHFR AGUBO AGYEJ AHHHB AHJVU AHZHX AIALX AIEXJ AIKHN AITUG AJOXV AKRWK ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD ASPBG AVWKF AXJTR AZFZN BJAXD BKOJK BLXMC CS3 DU5 EBS EFJIC EJD EO8 EO9 EP2 EP3 FDB FEDTE FGOYB FIRID FNPLU FYGXN G-2 G-Q GBLVA GBOLZ HLZ HVGLF HZ~ IHE J1W JJJVA KOM LG9 LY7 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. PQQKQ Q38 R2- RIG ROL RPZ RXW SBC SDF SDG SDP SES SET SEW SPC SPCBC SST SSV SSZ T5K TAE TN5 UHS VOH WH7 WUQ XPP ZMT ~G- ~S- 9DU AATTM AAXKI AAYWO AAYXX ABWVN ACLOT ACRPL ACVFH ADCNI ADNMO AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKYEP ANKPU APXCP CITATION EFKBS EFLBG ~HD |
| ID | FETCH-LOGICAL-c321t-14c37614efa48d6837dba0f6abd23fc79bd33f8568ac6b7b72dec4362e4fa2523 |
| ISICitedReferencesCount | 3 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001206598600001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0045-7906 |
| IngestDate | Sat Nov 29 03:04:42 EST 2025 Tue Nov 18 22:51:08 EST 2025 Tue Jun 18 08:50:55 EDT 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Hybrid metaheuristic algorithms Optimal tracking control Nonlinear systems Actor-critic neural network Reinforcement learning |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c321t-14c37614efa48d6837dba0f6abd23fc79bd33f8568ac6b7b72dec4362e4fa2523 |
| ParticipantIDs | crossref_primary_10_1016_j_compeleceng_2024_109179 crossref_citationtrail_10_1016_j_compeleceng_2024_109179 elsevier_sciencedirect_doi_10_1016_j_compeleceng_2024_109179 |
| PublicationCentury | 2000 |
| PublicationDate | May 2024 2024-05-00 |
| PublicationDateYYYYMMDD | 2024-05-01 |
| PublicationDate_xml | – month: 05 year: 2024 text: May 2024 |
| PublicationDecade | 2020 |
| PublicationTitle | Computers & electrical engineering |
| PublicationYear | 2024 |
| Publisher | Elsevier Ltd |
| Publisher_xml | – name: Elsevier Ltd |
| References | (b24) 1965 Mohammadi, Arefi, Setoodeh, Kaynak (b9) 2021; 554 Zargarzadeh, Dierks, Jagannathan (b18) 2015; 26 Dokeroglu, Sevinc, Kucukyilmaz, Cosar (b19) 2019; 137 Kim, Park, Yoo, Lee, Lee (b15) 2018; 51 Gao, Jiang (b5) 2022; 20 Wright (b25) 2010; 7 Lewis, Vrabie, Vamvoudakis (b7) 2012; 32 Abdel-Basset, Abdel-Fatah, Sangaiah (b21) 2018 Chou, Truong (b22) 2020; 389 Gao, Han (b28) 2012; 51 Shi, Yue, Xie (b10) 2020; 396 Itik (b2) 2016; 21 Mishra, Ghosh (b17) 2022 Jalaeian, Fateh, Rahimiyan (b4) 2021; 39 Zhu, Zhao, Li (b11) 2016; 10 Wen, Niu (b16) 2022; 606 Beheshti, Shamsuddin (b20) 2013; 5 Yu, Wang, Sun, Shen (b6) 2022; 53 Zhao, Vishal (b12) 2021; 15 Ye L, Li J, Wang C, Liu H, Liang B. Reinforcement Learning Tracking Control for Unknown Continuous Dynamic Systems. In: IEEE 10th data driven control and learning systems conference. 2021, p. 114–9. Khalil (b3) 2002 Singer, Nelder (b26) 2009; 4 Jalaeian-F M, Akbarzadeh-T MR, Akbarzadeh A, Ghaemi M. A dynamic-growing fuzzy-neuro controller, application to a 3PSP parallel robot. In: IEEE international conference on fuzzy systems. 2012, p. 1–6. Lewis, Vrabie, Syrmos (b1) 2012 Zhao (b8) 2020; 51 Molabahrami (b27) 2013 Lee, Park, Choi (b14) 2014; 26 Zhu (10.1016/j.compeleceng.2024.109179_b11) 2016; 10 10.1016/j.compeleceng.2024.109179_b13 Shi (10.1016/j.compeleceng.2024.109179_b10) 2020; 396 Zhao (10.1016/j.compeleceng.2024.109179_b8) 2020; 51 Mishra (10.1016/j.compeleceng.2024.109179_b17) 2022 Wen (10.1016/j.compeleceng.2024.109179_b16) 2022; 606 Gao (10.1016/j.compeleceng.2024.109179_b28) 2012; 51 Itik (10.1016/j.compeleceng.2024.109179_b2) 2016; 21 Jalaeian (10.1016/j.compeleceng.2024.109179_b4) 2021; 39 Gao (10.1016/j.compeleceng.2024.109179_b5) 2022; 20 Zhao (10.1016/j.compeleceng.2024.109179_b12) 2021; 15 Lee (10.1016/j.compeleceng.2024.109179_b14) 2014; 26 Kim (10.1016/j.compeleceng.2024.109179_b15) 2018; 51 (10.1016/j.compeleceng.2024.109179_b24) 1965 Wright (10.1016/j.compeleceng.2024.109179_b25) 2010; 7 Khalil (10.1016/j.compeleceng.2024.109179_b3) 2002 Yu (10.1016/j.compeleceng.2024.109179_b6) 2022; 53 Singer (10.1016/j.compeleceng.2024.109179_b26) 2009; 4 Zargarzadeh (10.1016/j.compeleceng.2024.109179_b18) 2015; 26 Lewis (10.1016/j.compeleceng.2024.109179_b1) 2012 Beheshti (10.1016/j.compeleceng.2024.109179_b20) 2013; 5 Lewis (10.1016/j.compeleceng.2024.109179_b7) 2012; 32 Mohammadi (10.1016/j.compeleceng.2024.109179_b9) 2021; 554 10.1016/j.compeleceng.2024.109179_b23 Dokeroglu (10.1016/j.compeleceng.2024.109179_b19) 2019; 137 Abdel-Basset (10.1016/j.compeleceng.2024.109179_b21) 2018 Molabahrami (10.1016/j.compeleceng.2024.109179_b27) 2013 Chou (10.1016/j.compeleceng.2024.109179_b22) 2020; 389 |
| References_xml | – volume: 396 start-page: 172 year: 2020 end-page: 178 ident: b10 article-title: Adaptive optimal tracking control for nonlinear continuous-time systems with time delay using value iteration algorithm publication-title: Neurocomputing – volume: 39 start-page: 200 year: 2021 end-page: 216 ident: b4 article-title: Bi-level adaptive computed-current impedance controller for electrically driven robots publication-title: Robotica – volume: 20 start-page: 1 year: 2022 end-page: 19 ident: b5 article-title: Learning-based adaptive optimal output regulation of linear and nonlinear systems: an overview publication-title: Control Theory Technol – volume: 389 year: 2020 ident: b22 article-title: A novel metaheuristic optimizer inspired by behavior of Jellfish in ocean publication-title: Appl Math Comput – volume: 32 start-page: 76 year: 2012 end-page: 105 ident: b7 article-title: Reinforcement learning and feedback control: Using natural decision methods to design optimal adaptive controllers publication-title: IEEE Control Syst Mag – volume: 554 start-page: 84 year: 2021 end-page: 98 ident: b9 article-title: Optimal tracking control based on reinforcement learning value iteration algorithm for time-delayed nonlinear systems with external disturbances and input constraints publication-title: Inform Sci – volume: 137 year: 2019 ident: b19 article-title: A survey on new generation metaheuristic algorithms publication-title: Comput Ind Eng – volume: 15 start-page: 260 year: 2021 end-page: 271 ident: b12 article-title: Neural network-based optimal tracking control for partially unknown discrete-time non-linear systems using reinforcement learning publication-title: IET Control Theory Appl – year: 1965 ident: b24 article-title: A simplex method for function minimization publication-title: Comput J – year: 2012 ident: b1 article-title: Optimal control – reference: Jalaeian-F M, Akbarzadeh-T MR, Akbarzadeh A, Ghaemi M. A dynamic-growing fuzzy-neuro controller, application to a 3PSP parallel robot. In: IEEE international conference on fuzzy systems. 2012, p. 1–6. – volume: 21 start-page: 400 year: 2016 end-page: 412 ident: b2 article-title: Optimal control of nonlinear systems with input constraints using linear time varying approximations publication-title: Nonlinear Anal Model Control – start-page: 185 year: 2018 end-page: 231 ident: b21 article-title: Metaheuristic algorithms: A comprehensive review publication-title: Computational intelligence for multimedia big data on the cloud with engineering applications – volume: 51 start-page: 257 year: 2018 end-page: 262 ident: b15 article-title: Deep reinforcement learning based finite-horizon optimal tracking control for nonlinear system publication-title: IFAC-PapersOnLine – volume: 606 start-page: 368 year: 2022 end-page: 379 ident: b16 article-title: Optimized tracking control based on reinforcement learning for a class of high-order unknown nonlinear dynamic systems publication-title: Inform Sci – volume: 53 start-page: 2815 year: 2022 end-page: 2827 ident: b6 article-title: Optimal control of nonlinear systems with unsymmetrical input constraints and its application to the UAV circumnavigation problem publication-title: IEEE Trans Syst Man Cybern A – volume: 10 start-page: 1339 year: 2016 end-page: 1347 ident: b11 article-title: Using reinforcement learning techniques to solve continuous-time non-linear optimal tracking problem without system dynamics publication-title: IET Control Theory Appl – volume: 5 start-page: 1 year: 2013 end-page: 35 ident: b20 article-title: A review of population-based meta-heuristic algorithms publication-title: Int J Adv Soft Comput Appl – start-page: 1 year: 2013 end-page: 15 ident: b27 article-title: Integral mean value method for solving a general nonlinear Fredholm integro-differential equation under the mixed conditions publication-title: Commun Numer Anal – year: 2002 ident: b3 article-title: Nonlinear systems – volume: 4 start-page: 2928 year: 2009 ident: b26 article-title: Nelder-Mead algorithm publication-title: Scholarpedia – start-page: 1 year: 2022 end-page: 20 ident: b17 article-title: Variable gain gradient descent-based reinforcement learning for robust optimal tracking control of uncertain nonlinear system with input constraints publication-title: Nonlinear Dynam – reference: Ye L, Li J, Wang C, Liu H, Liang B. Reinforcement Learning Tracking Control for Unknown Continuous Dynamic Systems. In: IEEE 10th data driven control and learning systems conference. 2021, p. 114–9. – volume: 26 start-page: 916 year: 2014 end-page: 932 ident: b14 article-title: Integral reinforcement learning for continuous-time input-affine nonlinear systems with simultaneous invariant explorations publication-title: IEEE Trans Neural Netw Learn Syst – volume: 7 start-page: 271 year: 2010 end-page: 276 ident: b25 article-title: Nelder, Mead, and the other simplex method publication-title: Doc Math – volume: 51 start-page: 2513 year: 2020 end-page: 2530 ident: b8 article-title: Neural network based optimal control tracking control of continuous time uncertain nonlinear system via reinforcement learning publication-title: Neural Process Lett – volume: 26 start-page: 2535 year: 2015 end-page: 2549 ident: b18 article-title: Optimal control of nonlinear continuous time system in strict feedback form publication-title: IEEE Trans Neural Netw Learn Syst – volume: 51 start-page: 259 year: 2012 end-page: 277 ident: b28 article-title: Implementing the Nelder–Mead simplex algorithm with adaptive parameters publication-title: Comput Optim Appl – volume: 32 start-page: 76 issue: 6 year: 2012 ident: 10.1016/j.compeleceng.2024.109179_b7 article-title: Reinforcement learning and feedback control: Using natural decision methods to design optimal adaptive controllers publication-title: IEEE Control Syst Mag doi: 10.1109/MCS.2012.2214134 – start-page: 185 year: 2018 ident: 10.1016/j.compeleceng.2024.109179_b21 article-title: Metaheuristic algorithms: A comprehensive review – year: 2002 ident: 10.1016/j.compeleceng.2024.109179_b3 – volume: 51 start-page: 257 issue: 25 year: 2018 ident: 10.1016/j.compeleceng.2024.109179_b15 article-title: Deep reinforcement learning based finite-horizon optimal tracking control for nonlinear system publication-title: IFAC-PapersOnLine doi: 10.1016/j.ifacol.2018.11.115 – start-page: 1 year: 2022 ident: 10.1016/j.compeleceng.2024.109179_b17 article-title: Variable gain gradient descent-based reinforcement learning for robust optimal tracking control of uncertain nonlinear system with input constraints publication-title: Nonlinear Dynam – volume: 20 start-page: 1 issue: 1 year: 2022 ident: 10.1016/j.compeleceng.2024.109179_b5 article-title: Learning-based adaptive optimal output regulation of linear and nonlinear systems: an overview publication-title: Control Theory Technol doi: 10.1007/s11768-022-00081-3 – volume: 554 start-page: 84 year: 2021 ident: 10.1016/j.compeleceng.2024.109179_b9 article-title: Optimal tracking control based on reinforcement learning value iteration algorithm for time-delayed nonlinear systems with external disturbances and input constraints publication-title: Inform Sci doi: 10.1016/j.ins.2020.11.057 – volume: 5 start-page: 1 issue: 1 year: 2013 ident: 10.1016/j.compeleceng.2024.109179_b20 article-title: A review of population-based meta-heuristic algorithms publication-title: Int J Adv Soft Comput Appl – volume: 51 start-page: 2513 issue: 3 year: 2020 ident: 10.1016/j.compeleceng.2024.109179_b8 article-title: Neural network based optimal control tracking control of continuous time uncertain nonlinear system via reinforcement learning publication-title: Neural Process Lett doi: 10.1007/s11063-020-10220-z – start-page: 1 year: 2013 ident: 10.1016/j.compeleceng.2024.109179_b27 article-title: Integral mean value method for solving a general nonlinear Fredholm integro-differential equation under the mixed conditions publication-title: Commun Numer Anal – volume: 15 start-page: 260 issue: 2 year: 2021 ident: 10.1016/j.compeleceng.2024.109179_b12 article-title: Neural network-based optimal tracking control for partially unknown discrete-time non-linear systems using reinforcement learning publication-title: IET Control Theory Appl doi: 10.1049/cth2.12037 – volume: 137 year: 2019 ident: 10.1016/j.compeleceng.2024.109179_b19 article-title: A survey on new generation metaheuristic algorithms publication-title: Comput Ind Eng doi: 10.1016/j.cie.2019.106040 – ident: 10.1016/j.compeleceng.2024.109179_b23 doi: 10.1109/FUZZ-IEEE.2012.6251315 – volume: 389 year: 2020 ident: 10.1016/j.compeleceng.2024.109179_b22 article-title: A novel metaheuristic optimizer inspired by behavior of Jellfish in ocean publication-title: Appl Math Comput – volume: 51 start-page: 259 issue: 1 year: 2012 ident: 10.1016/j.compeleceng.2024.109179_b28 article-title: Implementing the Nelder–Mead simplex algorithm with adaptive parameters publication-title: Comput Optim Appl doi: 10.1007/s10589-010-9329-3 – volume: 53 start-page: 2815 issue: 5 year: 2022 ident: 10.1016/j.compeleceng.2024.109179_b6 article-title: Optimal control of nonlinear systems with unsymmetrical input constraints and its application to the UAV circumnavigation problem publication-title: IEEE Trans Syst Man Cybern A doi: 10.1109/TSMC.2022.3220028 – volume: 26 start-page: 2535 issue: 10 year: 2015 ident: 10.1016/j.compeleceng.2024.109179_b18 article-title: Optimal control of nonlinear continuous time system in strict feedback form publication-title: IEEE Trans Neural Netw Learn Syst doi: 10.1109/TNNLS.2015.2441712 – volume: 21 start-page: 400 issue: 3 year: 2016 ident: 10.1016/j.compeleceng.2024.109179_b2 article-title: Optimal control of nonlinear systems with input constraints using linear time varying approximations publication-title: Nonlinear Anal Model Control doi: 10.15388/NA.2016.3.7 – volume: 4 start-page: 2928 issue: 7 year: 2009 ident: 10.1016/j.compeleceng.2024.109179_b26 article-title: Nelder-Mead algorithm publication-title: Scholarpedia doi: 10.4249/scholarpedia.2928 – volume: 606 start-page: 368 year: 2022 ident: 10.1016/j.compeleceng.2024.109179_b16 article-title: Optimized tracking control based on reinforcement learning for a class of high-order unknown nonlinear dynamic systems publication-title: Inform Sci doi: 10.1016/j.ins.2022.05.048 – volume: 7 start-page: 271 year: 2010 ident: 10.1016/j.compeleceng.2024.109179_b25 article-title: Nelder, Mead, and the other simplex method publication-title: Doc Math – volume: 10 start-page: 1339 issue: 12 year: 2016 ident: 10.1016/j.compeleceng.2024.109179_b11 article-title: Using reinforcement learning techniques to solve continuous-time non-linear optimal tracking problem without system dynamics publication-title: IET Control Theory Appl doi: 10.1049/iet-cta.2015.0769 – volume: 26 start-page: 916 issue: 5 year: 2014 ident: 10.1016/j.compeleceng.2024.109179_b14 article-title: Integral reinforcement learning for continuous-time input-affine nonlinear systems with simultaneous invariant explorations publication-title: IEEE Trans Neural Netw Learn Syst – ident: 10.1016/j.compeleceng.2024.109179_b13 doi: 10.1109/DDCLS52934.2021.9455473 – volume: 396 start-page: 172 year: 2020 ident: 10.1016/j.compeleceng.2024.109179_b10 article-title: Adaptive optimal tracking control for nonlinear continuous-time systems with time delay using value iteration algorithm publication-title: Neurocomputing doi: 10.1016/j.neucom.2018.07.098 – volume: 39 start-page: 200 issue: 2 year: 2021 ident: 10.1016/j.compeleceng.2024.109179_b4 article-title: Bi-level adaptive computed-current impedance controller for electrically driven robots publication-title: Robotica doi: 10.1017/S0263574720000314 – year: 2012 ident: 10.1016/j.compeleceng.2024.109179_b1 – year: 1965 ident: 10.1016/j.compeleceng.2024.109179_b24 article-title: A simplex method for function minimization publication-title: Comput J |
| SSID | ssj0004618 |
| Score | 2.3605013 |
| Snippet | This paper proposes an innovative method for achieving optimal tracking control in nonlinear continuous-time systems with input constraints. The method... |
| SourceID | crossref elsevier |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 109179 |
| SubjectTerms | Actor-critic neural network Hybrid metaheuristic algorithms Nonlinear systems Optimal tracking control Reinforcement learning |
| Title | Combining hybrid metaheuristic algorithms and reinforcement learning to improve the optimal control of nonlinear continuous-time systems with input constraints |
| URI | https://dx.doi.org/10.1016/j.compeleceng.2024.109179 |
| Volume | 116 |
| WOSCitedRecordID | wos001206598600001&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: PRVESC databaseName: ScienceDirect Freedom Collection - Elsevier customDbUrl: eissn: 1879-0755 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0004618 issn: 0045-7906 databaseCode: AIEXJ dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1La9wwEBZLUkp7KH3S9IUKvS0u66dk6GUpCW0PaSEp7M3ItpR12LUXrx2yv6bn_svOSPIjaUtTSi_GiJUl73yeh5j5hpA3npQqiCIIcvxYOgCKzOEhT52ZzISa8diTgdLNJtjxMV8s4i-TyfeuFuZixcqSX17Gm_8qahgDYWPp7F-Iu38oDMA9CB2uIHa43kjw8IWnuuvDdLnDcixsEi2WsjWUzFOxOqvqolmuDTlzLTV1aqZPCbseEmfokRb6uEFqz7QCxbLWTCImsR2zPwzHhqj1YFG2Vbt1sFO9JYe2ZXNFuWkb_MlWN6MwvFE9NYJtKbHVADQdeTRo5MCS2FuEJUQMq2I6XxeYhpAXJi9815uVEwjSd8jmMT0SJl__89qm69tTDS8YcgjNUVtXbjPkNmn1HSC_5sxyZxuNzRlWYRmu316lm_rNn8yDOak4R-lu8KXgbd7i6kiq5ZqmNtfYt09wTVwSc20hVIZAe99jYQw2YH_-8XDxaVSE6xqzb_d4m7wekgl_s-CvnaGRg3N6n9yzkQmdG0Q9IBNZPiR3R3yVj8i3HlvUYItewRYdsEUBW_QKtmiHLdpU1GKLAraoxRa12KKVoj226DVsUYstitiiGlt0hK3H5OvR4en7D47t8OFkvuc2jhtkYODcQCoR8DziPstTMVORSHPPVxmL09z3FQ8jLrIoZSnzcpkF4HOBBhFe6PlPyB5sST4lFHQNeFqR4K7iQawwrokYc_MolAp8XnFAePdXJ5mlv8fNrZIuz_E8GUkpQSklRkoHxOunbgwHzE0mvevkmVhn1jipCYDxz9Of_dv05-TO8FW9IHtN3cqX5FZ20RTb-pWF7g-RMtfS |
| linkProvider | Elsevier |
| 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=Combining+hybrid+metaheuristic+algorithms+and+reinforcement+learning+to+improve+the+optimal+control+of+nonlinear+continuous-time+systems+with+input+constraints&rft.jtitle=Computers+%26+electrical+engineering&rft.au=Khalili+Amirabadi%2C+Roya&rft.au=Solaymani+Fard%2C+Omid&rft.date=2024-05-01&rft.pub=Elsevier+Ltd&rft.issn=0045-7906&rft.eissn=1879-0755&rft.volume=116&rft_id=info:doi/10.1016%2Fj.compeleceng.2024.109179&rft.externalDocID=S0045790624001071 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0045-7906&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0045-7906&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0045-7906&client=summon |