{"id":"https://openalex.org/W4320802486","doi":"https://doi.org/10.1109/ccdc55256.2022.10033516","title":"Data-Driven Optimal Controller Design for Maglev Train: Q-Learning Method","display_name":"Data-Driven Optimal Controller Design for Maglev Train: Q-Learning Method","publication_year":2022,"publication_date":"2022-08-15","ids":{"openalex":"https://openalex.org/W4320802486","doi":"https://doi.org/10.1109/ccdc55256.2022.10033516"},"language":"en","primary_location":{"id":"doi:10.1109/ccdc55256.2022.10033516","doi":"https://doi.org/10.1109/ccdc55256.2022.10033516","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ccdc55256.2022.10033516","pdf_url":null,"source":{"id":"https://openalex.org/S4363607715","display_name":"Chinese Control and Decision Conference (CCDC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 34th Chinese Control and Decision Conference (CCDC)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100717537","display_name":"Xin Liang","orcid":"https://orcid.org/0000-0001-9984-1819","observed_orcids":["https://orcid.org/0000-0001-9984-1819"]},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liang Xin","raw_affiliation_strings":["National University of Defense Technology,College of Intelligence Science and Technology,Changsha,China,410073"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National University of Defense Technology,College of Intelligence Science and Technology,Changsha,China,410073","institution_ids":["https://openalex.org/I170215575"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Hongwei Jiang","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hongwei Jiang","raw_affiliation_strings":["CRRC Zhuzhou Locomotive Co.,Ltd,Zhuzhou,China,412001"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"CRRC Zhuzhou Locomotive Co.,Ltd,Zhuzhou,China,412001","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101694439","display_name":"Tao Wen","orcid":"https://orcid.org/0000-0001-7971-9242","observed_orcids":["https://orcid.org/0000-0001-7971-9242"]},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tao Wen","raw_affiliation_strings":["National University of Defense Technology,College of Intelligence Science and Technology,Changsha,China,410073"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National University of Defense Technology,College of Intelligence Science and Technology,Changsha,China,410073","institution_ids":["https://openalex.org/I170215575"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100438093","display_name":"Zhiqiang Long","orcid":"https://orcid.org/0000-0003-4275-2377","observed_orcids":["https://orcid.org/0000-0003-4275-2377","https://orcid.org/0000-0001-5129-6874"]},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiqiang Long","raw_affiliation_strings":["National University of Defense Technology,College of Intelligence Science and Technology,Changsha,China,410073"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National University of Defense Technology,College of Intelligence Science and Technology,Changsha,China,410073","institution_ids":["https://openalex.org/I170215575"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.3484,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.36761066,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":93},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12794","display_name":"Adaptive Dynamic Programming Control","score":0.9692000150680542,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12794","display_name":"Adaptive Dynamic Programming Control","score":0.9692000150680542,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11749","display_name":"Iterative Learning Control Systems","score":0.01080000028014183,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10462","display_name":"Reinforcement Learning in Robotics","score":0.00419999985024333,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/maglev-train","display_name":"maglev train","score":0.8870000243186951},{"id":"https://openalex.org/keywords/q-learning","display_name":"Q-learning","score":0.8209999799728394},{"id":"https://openalex.org/keywords/data-driven-optimal-control","display_name":"data-driven optimal control","score":0.6880000233650208},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"reinforcement learning","score":0.7850000262260437},{"id":"https://openalex.org/keywords/model-free-control","display_name":"model-free control","score":0.7210000157356262},{"id":"https://openalex.org/keywords/model-uncertainty","display_name":"model uncertainty","score":0.38100001215934753},{"id":"https://openalex.org/keywords/feedback-gain-tuning","display_name":"feedback gain tuning","score":0.5960000157356262},{"id":"https://openalex.org/keywords/setpoint-tracking","display_name":"reference tracking","score":0.8159999847412109},{"id":"https://openalex.org/keywords/unstable-nonlinear-systems","display_name":"nonlinear unstable systems","score":0.7099999785423279}],"concepts":[{"id":"https://openalex.org/C196781063","wikidata":"https://www.wikidata.org/wiki/Q160047","display_name":"Maglev","level":2,"score":0.8820439577102661},{"id":"https://openalex.org/C47446073","wikidata":"https://www.wikidata.org/wiki/Q5165890","display_name":"Control theory (sociology)","level":3,"score":0.7178292870521545},{"id":"https://openalex.org/C203479927","wikidata":"https://www.wikidata.org/wiki/Q5165939","display_name":"Controller (irrigation)","level":2,"score":0.6500547528266907},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6366547346115112},{"id":"https://openalex.org/C133731056","wikidata":"https://www.wikidata.org/wiki/Q4917288","display_name":"Control engineering","level":1,"score":0.5216425061225891},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.5189213752746582},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.5163301229476929},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.5058900713920593},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.5046838521957397},{"id":"https://openalex.org/C161362739","wikidata":"https://www.wikidata.org/wiki/Q2301555","display_name":"Open-loop controller","level":3,"score":0.4497966766357422},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.4276133179664612},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.26312339305877686},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.22766900062561035},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.1643090546131134},{"id":"https://openalex.org/C3019251811","wikidata":"https://www.wikidata.org/wiki/Q5135346","display_name":"Closed loop","level":2,"score":0.08887746930122375},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.08304697275161743},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","level":1,"score":0.0},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.0},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ccdc55256.2022.10033516","doi":"https://doi.org/10.1109/ccdc55256.2022.10033516","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ccdc55256.2022.10033516","pdf_url":null,"source":{"id":"https://openalex.org/S4363607715","display_name":"Chinese Control and Decision Conference (CCDC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 34th Chinese Control and Decision Conference (CCDC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4943999946117401,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W2013895638","https://openalex.org/W2040871222","https://openalex.org/W2139593900","https://openalex.org/W2150771917","https://openalex.org/W2152161277","https://openalex.org/W2153028751","https://openalex.org/W2165726932","https://openalex.org/W2971630633","https://openalex.org/W2972180738"],"related_works":["https://openalex.org/W4401586442","https://openalex.org/W4389230183","https://openalex.org/W7117583949","https://openalex.org/W7155694012","https://openalex.org/W7128923063","https://openalex.org/W4403098766","https://openalex.org/W3008055000","https://openalex.org/W2383176515","https://openalex.org/W7148187943","https://openalex.org/W2160972050"],"abstract_inverted_index":{"The":[0,81],"maglev":[1,26],"train":[2,27],"is":[3,44],"an":[4],"open-loop":[5],"and":[6,46,114,144,164],"unstable":[7],"complex":[8,32],"nonlinear":[9],"system.":[10,169],"Generally,":[11],"design":[12],"of":[13,25,91,110,149,157,166],"offline":[14,48],"controllers":[15],"based":[16,68,98],"on":[17,69,99],"a":[18,63],"single":[19],"operating":[20],"state.":[21],"However,":[22],"the":[23,38,42,57,70,88,92,100,108,119,127,134,138,147,154,158,162,167],"system":[24,39,122,135],"will":[28,49,55],"influence":[29],"by":[30,142],"various":[31],"factors":[33],"in":[34,73,76,130],"actual":[35],"operation.":[36],"When":[37],"model":[40,89,123],"changes,":[41],"controller":[43,82],"designed":[45],"tuned":[47],"suffer":[50],"severe":[51],"performance":[52],"degradation":[53],"that":[54,121],"affect":[56],"system\u2019s":[58,101],"stability.":[59],"This":[60],"paper":[61,132],"proposes":[62],"Data-Ddriven":[64],"Optimal":[65],"Controller":[66],"(DDOC)":[67],"Q-learning":[71],"theory":[72],"reinforcement":[74],"learning":[75],"response":[77],"to":[78,86],"this":[79,131],"problem.":[80],"does":[83],"not":[84],"need":[85],"know":[87],"information":[90],"controlled":[93],"object,":[94],"only":[95],"calculates":[96],"iteratively":[97],"real-time":[102,155],"input,":[103],"output":[104],"data,":[105],"which":[106],"has":[107],"advantages":[109],"fewer":[111],"tuning":[112],"parameters":[113,148],"fast":[115],"convergence":[116],"speed.":[117],"For":[118],"problem":[120],"change":[124],"during":[125],"operation,":[126],"method":[128],"proposed":[129],"makes":[133],"accurately":[136],"track":[137],"given":[139],"reference":[140],"signal":[141],"dynamically":[143],"rapidly":[145],"changing":[146],"feedback":[150],"gain":[151],"matrix":[152],"through":[153],"data":[156],"system,":[159],"thus":[160],"ensuring":[161],"stability":[163],"reliability":[165],"control":[168]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2023-02-15T00:00:00"}