{"?xml":{"@version":"1.0"},"edm:RDF":{"@xmlns:dc":"http://purl.org/dc/elements/1.1/","@xmlns:edm":"http://www.europeana.eu/schemas/edm/","@xmlns:wgs84_pos":"http://www.w3.org/2003/01/geo/wgs84_pos","@xmlns:foaf":"http://xmlns.com/foaf/0.1/","@xmlns:rdaGr2":"http://rdvocab.info/ElementsGr2","@xmlns:oai":"http://www.openarchives.org/OAI/2.0/","@xmlns:owl":"http://www.w3.org/2002/07/owl#","@xmlns:rdf":"http://www.w3.org/1999/02/22-rdf-syntax-ns#","@xmlns:ore":"http://www.openarchives.org/ore/terms/","@xmlns:skos":"http://www.w3.org/2004/02/skos/core#","@xmlns:dcterms":"http://purl.org/dc/terms/","edm:WebResource":[{"@rdf:about":"http://www.dlib.si/stream/URN:NBN:SI:DOC-PUJV2OEC/5545bb88-42f9-4bc0-988b-e74fea6cde81/PDF","dcterms:extent":"3058 KB"},{"@rdf:about":"http://www.dlib.si/stream/URN:NBN:SI:DOC-PUJV2OEC/8f2313df-2c49-4171-b420-0895b2ba52bc/TEXT","dcterms:extent":"0 KB"}],"edm:ProvidedCHO":{"@rdf:about":"URN:NBN:SI:DOC-PUJV2OEC","dcterms:issued":"2026","dc:creator":["Tang, Jinlong","Wan, Jun","Wang, Kehong","Yun, Nuo","Zhang, Xiao Yong","Zhou, ZiHao"],"dc:format":[{"@xml:lang":"sl","#text":"številka:1/2"},{"@xml:lang":"sl","#text":"letnik:72"},{"@xml:lang":"sl","#text":"str. 40-51"}],"dc:identifier":["ISSN:0039-2480","DOI:10.5545/sv-jme.2025.1489","COBISSID_HOST:272814339","URN:URN:NBN:SI:doc-PUJV2OEC"],"dc:language":"en","dc:publisher":[{"@xml:lang":"sl","#text":"= Association of Mechanical Engineers and Technicians of Slovenia etc."},{"@xml:lang":"sl","#text":"Zveza strojnih inženirjev in tehnikov Slovenije etc."}],"dc:source":{"@xml:lang":"sl","#text":"Strojniški vestnik"},"dc:subject":[{"@xml:lang":"en","#text":"external force estimation"},{"@xml:lang":"en","#text":"friction compensation"},{"@xml:lang":"en","#text":"fuzzy neural networks"},{"@xml:lang":"sl","#text":"kompenzacija trenja"},{"@xml:lang":"sl","#text":"mehke nevronske mreže"},{"@xml:lang":"sl","#text":"ocena zunanjih sil"},{"@xml:lang":"en","#text":"Stribeck model"},{"@xml:lang":"sl","#text":"Stribeckov model"}],"dc:title":{"@xml:lang":"sl","#text":"Friction compensation and external force estimation for robotic systems using a fuzzy neural network approach|"},"dc:description":[{"@xml:lang":"sl","#text":"To address inaccurate external force estimation caused by nonlinear friction in robotic systems, this paper proposes a friction compensation and external force estimation method based on an adaptive neuro-fuzzy inference system (ANFIS). The approach integrates Stribeck friction modeling with a Takagi–Sugeno fuzzy inference structure to identify joint friction parameters from measured data. Experimental results show that ANFIS yields lower identification errors and better generalization performance than baseline methods including fuzzy neural networks, particle swarm optimization, and least squares. The implemented feedforward compensation strategy achieves maximum torque errors of 0.263 Nm and 0.184 Nm for the two joints, lower than those obtained by the compared approaches. By incorporating the identified friction model into a generalized momentum observer with median and Butterworth filtering, the proposed method reduces the root mean square error and maximum absolute error by 18.3 % and 27.9 %, respectively, and achieves a coefficient of determination (R2) of 0.994. In collision detection tests, the method identifies impact events with reduced false alarm rates under the same experimental settings, supporting its applicability to high-precision force control in robotic applications"},{"@xml:lang":"sl","#text":"Za odpravo nenatančne ocene zunanjih sil, ki je posledica nelinearnega trenja v robotskih sistemih, je predlagana metoda kompenzacije trenja in ocene zunanjih sil na osnovi prilagodljivega nevro-mehkega inferenčnega sistema (ANFIS). Pristop združuje Stribeckov model trenja s Takagi–Sugeno mehko-inferenčno strukturo za identifikacijo parametrov trenja v sklepih na podlagi izmerjenih podatkov. Eksperimentalni rezultati kažejo, da ANFIS dosega manjše identifikacijske napake in boljšo sposobnost posploševanja v primerjavi z referenčnimi metodami, kot so mehke nevronske mreže, optimizacija z rojem delcev in metoda najmanjših kvadratov. Implementirana kompenzacijska strategija vnaprejšnjega (feedforward) krmiljenja doseže največje napake navora 0,263 Nm in 0,184 Nm za oba sklepa, kar je manj kot pri primerjanih pristopih. Z vključitvijo identificiranega modela trenja v posplošeni opazovalnik momenta s filtrom na osnovi mediane in Butterworthovim filtriranjem predlagana metoda zmanjša srednjo kvadratno napako (RMSE) in največjo absolutno napako za 18,3 % oziroma 27,9 % ter doseže koeficient determinacije (R2) 0,994. V preizkusih zaznavanja trkov metoda zazna udarne dogodke z nižjo stopnjo lažnih alarmov pri enakih eksperimentalnih pogojih, kar potrjuje njeno uporabnost za visoko natančno krmiljenje sil v robotskih aplikacijah"}],"edm:type":"TEXT","dc:type":[{"@xml:lang":"sl","#text":"znanstveno časopisje"},{"@xml:lang":"en","#text":"journals"},{"@rdf:resource":"http://www.wikidata.org/entity/Q361785"}]},"ore:Aggregation":{"@rdf:about":"http://www.dlib.si/?URN=URN:NBN:SI:DOC-PUJV2OEC","edm:aggregatedCHO":{"@rdf:resource":"URN:NBN:SI:DOC-PUJV2OEC"},"edm:isShownBy":{"@rdf:resource":"http://www.dlib.si/stream/URN:NBN:SI:DOC-PUJV2OEC/5545bb88-42f9-4bc0-988b-e74fea6cde81/PDF"},"edm:rights":{"@rdf:resource":"http://rightsstatements.org/vocab/InC/1.0/"},"edm:provider":"Slovenian National E-content Aggregator","edm:intermediateProvider":{"@xml:lang":"en","#text":"National and University Library of Slovenia"},"edm:dataProvider":{"@xml:lang":"sl","#text":"Univerza v Ljubljani, Fakulteta za strojništvo"},"edm:object":{"@rdf:resource":"http://www.dlib.si/streamdb/URN:NBN:SI:DOC-PUJV2OEC/maxi/edm"},"edm:isShownAt":{"@rdf:resource":"http://www.dlib.si/details/URN:NBN:SI:DOC-PUJV2OEC"}}}}