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Inftech."],"published-print":{"date-parts":[[2022,4]]},"abstract":"<jats:title>Zusammenfassung<\/jats:title><jats:p>Datenbasierte Sch\u00e4tzalgorithmen (z.\u202fB. k\u00fcnstliche neuronale Netze oder Support Vector Machines) sind eine robuste Alternative zu konventionellen, physikalischen Modellen f\u00fcr adaptive Regler. Dieser Typ Algorithmus kann anhand von empirischen Daten nichtlineare und zeitvariante dynamische Systeme identifizieren sowie zuk\u00fcnftige Ausgangsgr\u00f6\u00dfen prognostizieren. Tiefere Kenntnisse \u00fcber die Physik des technischen Systems sind somit nicht mehr notwendig, und es k\u00f6nnen einfach und schnell Modelle abgeleitet werden. Speziell f\u00fcr Industrieroboter er\u00f6ffnen datenbasierte Sch\u00e4tzalgorithmen neue M\u00f6glichkeiten. So muss die nichtlineare Dynamik des Roboters je nach Pose und Last nicht durch komplexe kinematische Gleichungen berechnet, sondern kann aufgrund von Sensordaten gesch\u00e4tzt werden. Dies spart Berechnungszeit bei gleichbleibend hoher Modellg\u00fcte. Des Weiteren kann der Regler deutlich schneller bei ge\u00e4nderten Randbedingungen des Roboters, wie beispielsweise stark unterschiedlichen mechanischen Lasten am Endeffektor, adaptiert werden. In diesem Beitrag wird exemplarisch die Auslegung eines datenbasierten, modellpr\u00e4diktiven Reglers sowohl f\u00fcr einen sechs- als auch f\u00fcr einen einachsigen Gelenkpr\u00fcfstand diskutiert. Hierbei erfolgen der Entwurf und die Validierung des Reglers anhand einer Co-Simulation bestehend aus Mehrk\u00f6rper- und Systemsimulation. Designparameter der Regelung (Gr\u00f6\u00dfe des Modells, Trainingsdaten, Optimierer etc.) werden diskutiert. Schlie\u00dflich erfolgt eine experimentelle Validierung an einem einachsigen Gelenkpr\u00fcfstand.<\/jats:p>","DOI":"10.1007\/s00502-022-01000-9","type":"journal-article","created":{"date-parts":[[2022,3,8]],"date-time":"2022-03-08T12:03:20Z","timestamp":1646741000000},"page":"250-259","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Datenbasierte, adaptive Regelung von Servomotoren f\u00fcr Industrieroboter","Data-driven, adaptive control of servo drives for industrial robots"],"prefix":"10.1007","volume":"139","author":[{"given":"Christopher","family":"Ranisch","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Heiko","family":"Koch","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Timm","family":"Streul","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,3,8]]},"reference":[{"key":"1000_CR1","doi-asserted-by":"publisher","first-page":"521","DOI":"10.1016\/j.procir.2019.04.053","volume":"82","author":"H Celikag","year":"2019","unstructured":"Celikag\u00a0H, Sims\u00a0ND, Ozturk\u00a0E (2019) Chatter suppression in robotic milling by control of configuration dependent dynamics. 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