{"id":"https://openalex.org/W4403534497","doi":"https://doi.org/10.1109/codit62066.2024.10708157","title":"Adaptive Velocity Estimators for Learning Control","display_name":"Adaptive Velocity Estimators for Learning Control","publication_year":2024,"publication_date":"2024-07-01","ids":{"openalex":"https://openalex.org/W4403534497","doi":"https://doi.org/10.1109/codit62066.2024.10708157"},"language":"en","primary_location":{"id":"doi:10.1109/codit62066.2024.10708157","is_oa":false,"landing_page_url":"https://doi.org/10.1109/codit62066.2024.10708157","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 10th International Conference on Control, Decision and Information Technologies (CoDIT)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5054931604","display_name":"Daniel Nikovski","orcid":"https://orcid.org/0000-0003-2919-645X"},"institutions":[{"id":"https://openalex.org/I4210159266","display_name":"Mitsubishi Electric (United States)","ror":"https://ror.org/053jnhe44","country_code":"US","type":"company","lineage":["https://openalex.org/I1306287861","https://openalex.org/I4210133125","https://openalex.org/I4210159266"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Daniel Nikovski","raw_affiliation_strings":["Mitsubishi Electric Research Laboratories (MERL),Cambridge,MA,USA,02139"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Mitsubishi Electric Research Laboratories (MERL),Cambridge,MA,USA,02139","institution_ids":["https://openalex.org/I4210159266"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5032010124","display_name":"William Yerazunis","orcid":null},"institutions":[{"id":"https://openalex.org/I4210159266","display_name":"Mitsubishi Electric (United States)","ror":"https://ror.org/053jnhe44","country_code":"US","type":"company","lineage":["https://openalex.org/I1306287861","https://openalex.org/I4210133125","https://openalex.org/I4210159266"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"William Yerazunis","raw_affiliation_strings":["Mitsubishi Electric Research Laboratories (MERL),Cambridge,MA,USA,02139"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Mitsubishi Electric Research Laboratories (MERL),Cambridge,MA,USA,02139","institution_ids":["https://openalex.org/I4210159266"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210159266"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1062","last_page":"1067"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11749","display_name":"Iterative Learning Control Systems","score":0.9904000163078308,"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/T11749","display_name":"Iterative Learning Control Systems","score":0.9904000163078308,"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/T10791","display_name":"Advanced Control Systems Optimization","score":0.9851999878883362,"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/T11081","display_name":"Advanced Control Systems Design","score":0.9505000114440918,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.6503477692604065},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.604783296585083},{"id":"https://openalex.org/keywords/adaptive-control","display_name":"Adaptive control","score":0.5341697335243225},{"id":"https://openalex.org/keywords/control","display_name":"Control (management)","score":0.41483911871910095},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.374060720205307},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3340255320072174},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.27450478076934814},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.23135006427764893}],"concepts":[{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.6503477692604065},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.604783296585083},{"id":"https://openalex.org/C107464732","wikidata":"https://www.wikidata.org/wiki/Q235781","display_name":"Adaptive control","level":3,"score":0.5341697335243225},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.41483911871910095},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.374060720205307},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3340255320072174},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.27450478076934814},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.23135006427764893}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/codit62066.2024.10708157","is_oa":false,"landing_page_url":"https://doi.org/10.1109/codit62066.2024.10708157","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 10th International Conference on Control, Decision and Information Technologies (CoDIT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":6,"referenced_works":["https://openalex.org/W1517774440","https://openalex.org/W2041434319","https://openalex.org/W2138898866","https://openalex.org/W2150823344","https://openalex.org/W2170031322","https://openalex.org/W6773842061"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W3046775127","https://openalex.org/W3107602296","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W4364306694","https://openalex.org/W4312192474","https://openalex.org/W4283697347"],"abstract_inverted_index":{"The":[0,47,93],"paper":[1],"proposes":[2],"a":[3,22,37],"method":[4],"for":[5],"learning":[6,120],"velocity":[7,45,70,76],"estimators,":[8,71],"in":[9,64,73,82,116],"the":[10,44,53,56,101],"form":[11],"of":[12,36,43,55,75,84],"finite":[13],"impulse":[14],"response":[15],"(FIR)":[16],"filters,":[17],"from":[18,21],"data":[19],"collected":[20],"system":[23,58],"equipped":[24],"with":[25,66,118],"quantizing":[26],"position":[27],"encoders":[28],"that":[29],"is":[30],"to":[31,52,99,106],"be":[32,114],"controlled":[33,57],"by":[34],"means":[35],"full-state":[38],"feedback":[39],"controller":[40,110],"making":[41],"use":[42],"estimates.":[46],"resulting":[48],"estimators":[49,96],"are":[50,97,104],"tailored":[51],"properties":[54],"and":[59,112],"show":[60],"empirically":[61],"superior":[62],"performance":[63],"comparison":[65],"commonly":[67],"used":[68,115],"baseline":[69],"both":[72],"terms":[74,83],"estimation":[77],"error":[78],"as":[79,81],"well":[80],"reduced":[85],"regulation":[86],"cost":[87],"when":[88],"tested":[89],"on":[90,108],"control":[91,121],"problems.":[92],"proposed":[94],"adaptive":[95],"resistant":[98],"overfitting":[100],"training":[102],"data,":[103],"easy":[105],"implement":[107],"embedded":[109],"devices,":[111],"can":[113],"conjunction":[117],"various":[119],"methods.":[122]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}