{"id":"https://openalex.org/W4206551089","doi":"https://doi.org/10.1080/00207179.2022.2029945","title":"Safe control of nonlinear systems in LPV framework using model-based reinforcement learning","display_name":"Safe control of nonlinear systems in LPV framework using model-based reinforcement learning","publication_year":2022,"publication_date":"2022-01-17","ids":{"openalex":"https://openalex.org/W4206551089","doi":"https://doi.org/10.1080/00207179.2022.2029945"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1080/00207179.2022.2029945","pdf_url":null,"source":{"id":"https://openalex.org/S88061139","display_name":"International Journal of Control","issn_l":"0020-7179","issn":["0020-7179","1366-5820"],"is_oa":false,"is_in_doaj":false,"is_indexed_in_scopus":true,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},"type":"article","type_crossref":"journal-article","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/A5058947568","display_name":"Yajie Bao","orcid":"https://orcid.org/0000-0001-8773-926X"},"institutions":[{"id":"https://openalex.org/I165733156","display_name":"University of Georgia","ror":"https://ror.org/00te3t702","country_code":"US","type":"funder","lineage":["https://openalex.org/I165733156"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yajie Bao","raw_affiliation_strings":["School of Electrical & Computer Engineering, The University of Georgia, Athens, GA, USA"],"affiliations":[{"raw_affiliation_string":"School of Electrical & Computer Engineering, The University of Georgia, Athens, GA, USA","institution_ids":["https://openalex.org/I165733156"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5086596463","display_name":"Javad Mohammadpour Velni","orcid":"https://orcid.org/0000-0001-8546-221X"},"institutions":[{"id":"https://openalex.org/I165733156","display_name":"University of Georgia","ror":"https://ror.org/00te3t702","country_code":"US","type":"funder","lineage":["https://openalex.org/I165733156"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Javad Mohammadpour Velni","raw_affiliation_strings":["School of Electrical & Computer Engineering, The University of Georgia, Athens, GA, USA"],"affiliations":[{"raw_affiliation_string":"School of Electrical & Computer Engineering, The University of Georgia, Athens, GA, USA","institution_ids":["https://openalex.org/I165733156"]}]}],"institution_assertions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5058947568"],"corresponding_institution_ids":["https://openalex.org/I165733156"],"apc_list":null,"apc_paid":null,"fwci":1.508,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.797272,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":90},"biblio":{"volume":"96","issue":"4","first_page":"1079","last_page":"1090"},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.998,"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/T10876","display_name":"Fault Detection and Control Systems","score":0.998,"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/T14011","display_name":"Elevator Systems and Control","score":0.9949,"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/T11372","display_name":"Hydraulic and Pneumatic Systems","score":0.9946,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[],"concepts":[{"id":"https://openalex.org/C47446073","wikidata":"https://www.wikidata.org/wiki/Q5165890","display_name":"Control theory (sociology)","level":3,"score":0.67678094},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.66449267},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.5970757},{"id":"https://openalex.org/C72434380","wikidata":"https://www.wikidata.org/wiki/Q230930","display_name":"State space","level":2,"score":0.50083756},{"id":"https://openalex.org/C133731056","wikidata":"https://www.wikidata.org/wiki/Q4917288","display_name":"Control engineering","level":1,"score":0.48454085},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.46511748},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4502003},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.42351624},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.37590626},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.35580245},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1780523},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1080/00207179.2022.2029945","pdf_url":null,"source":{"id":"https://openalex.org/S88061139","display_name":"International Journal of Control","issn_l":"0020-7179","issn":["0020-7179","1366-5820"],"is_oa":false,"is_in_doaj":false,"is_indexed_in_scopus":true,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.66,"display_name":"Peace, justice, and strong institutions","id":"https://metadata.un.org/sdg/16"}],"grants":[],"datasets":[],"versions":[],"referenced_works_count":22,"referenced_works":["https://openalex.org/W1593786937","https://openalex.org/W1872197921","https://openalex.org/W1989317569","https://openalex.org/W2012587148","https://openalex.org/W2017957151","https://openalex.org/W2053572490","https://openalex.org/W2225156818","https://openalex.org/W2780439059","https://openalex.org/W2887532679","https://openalex.org/W2892521964","https://openalex.org/W2923554444","https://openalex.org/W2963221646","https://openalex.org/W2966735560","https://openalex.org/W2972496728","https://openalex.org/W2981389409","https://openalex.org/W3093010610","https://openalex.org/W3101442004","https://openalex.org/W3125192641","https://openalex.org/W3156919398","https://openalex.org/W4200584179","https://openalex.org/W4205689302","https://openalex.org/W4243521357"],"related_works":["https://openalex.org/W4362501864","https://openalex.org/W4306904969","https://openalex.org/W4297873223","https://openalex.org/W4225571923","https://openalex.org/W3212257828","https://openalex.org/W3009457412","https://openalex.org/W2999580272","https://openalex.org/W2992629954","https://openalex.org/W2350784623","https://openalex.org/W2126211886"],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2],"a":[3,33,118,124],"safe":[4],"model-based":[5],"reinforcement":[6],"learning":[7],"(MBRL)":[8],"approach":[9,27,135],"to":[10,31,54,70,86,103],"control":[11,56,125,140],"nonlinear":[12],"systems":[13],"described":[14],"by":[15],"linear":[16],"parameter-varying":[17,119],"(LPV)":[18],"models.":[19],"A":[20],"variational":[21],"Bayesian":[22],"inference":[23],"Neural":[24],"Network":[25],"(BNN)":[26],"is":[28,48,99],"first":[29],"employed":[30],"learn":[32,55],"state-space":[34],"model":[35,47,69,96,130],"with":[36,61],"uncertainty":[37],"quantification":[38],"from":[39,43],"input-output":[40],"data":[41],"collected":[42],"the":[44,46,59,67,81,92,105,108,133],"system;":[45],"then":[49],"utilised":[50],"for":[51,58,74],"training":[52],"MBRL":[53,65],"actions":[57],"system":[60,122],"safety":[62,78],"guarantees.":[63],"Specifically,":[64],"employs":[66],"BNN":[68],"generate":[71],"simulation":[72,102,112,129],"environments":[73],"training,":[75],"which":[76],"avoids":[77],"violations":[79],"in":[80,101],"exploration":[82],"stage.":[83],"To":[84],"adapt":[85],"dynamically":[87],"varying":[88],"environments,":[89],"knowledge":[90],"on":[91,117],"evolution":[93],"of":[94,111],"LPV":[95],"scheduling":[97],"variables":[98],"incorporated":[100],"reduce":[104],"discrepancy":[106],"between":[107],"transition":[109],"distributions":[110],"and":[113,123],"real":[114],"environments.":[115],"Experiments":[116],"double":[120],"integrator":[121],"moment":[126],"gyroscope":[127],"(CMG)":[128],"demonstrate":[131],"that":[132],"proposed":[134],"can":[136],"safely":[137],"achieve":[138],"desired":[139],"performance.":[141]},"abstract_inverted_index_v3":null,"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W4206551089","counts_by_year":[{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":3}],"updated_date":"2025-02-21T01:52:51.388393","created_date":"2022-01-26"}