{"48571bf42f9b3e1b94b541f568cd4f0fisw-bibliothek":{"DOI":"10.23919/ECC64448.2024.10591213","ISBN":"","ISSN":"","URL":"","abstract":"Rack-and-pinion drives are commonly used in large machine tools to provide linear motion of heavy loads over long travel distances. A key concern in this context is the achievable path accuracy, which is limited by assembly and manufacturing tolerances of the gearing components in conjunction with load-dependent deformation and the inherent backlash of the system. To address this issue, this paper presents a method for robust modeling of the individual and state-dependent transmission errors of a drive utilizing a two-stage machine learning approach. Based on this, the position control is extended to include an error compensation, which suppresses the modeled deviations in the mechanical system including the position-dependent backlash. The achievable increase in path accuracy as well as the robustness of the approach are evaluated and quantified by an experimental validation on a system with industry standard components.","annote":"","author":[{"family":"Steinle","given":"Lukas"},{"family":"Leipe","given":"Valentin"},{"family":"Lechler","given":"Armin"},{"family":"Veri","given":"Alexander"}],"citation-label":"10591213","collection-editor":[],"collection-title":"","container-author":[],"container-title":"2024 European Control Conference (ECC)","documents":[],"edition":"","editor":[],"event-date":{"date-parts":[["2024","June"]],"literal":"2024"},"event-place":"","id":"48571bf42f9b3e1b94b541f568cd4f0fisw-bibliothek","interhash":"fc115f1ff8efe474a50ffb3153d44a85","intrahash":"48571bf42f9b3e1b94b541f568cd4f0f","issue":"","issued":{"date-parts":[["2024","June"]],"literal":"2024"},"keyword":"compensation drive error feed isw learning machine tool","misc":{"doi":"10.23919/ECC64448.2024.10591213"},"note":"","number":"","number-of-pages":"6","page":"2441-2447","page-first":"2441","publisher":"","publisher-place":"","status":"","title":"Learning Compensation of the State-Dependent Transmission Errors in Rack-and-Pinion Drives","type":"paper-conference","username":"isw-bibliothek","version":"","volume":""},"f698e236a380faddfb8f96d98b28f5bcinue":{"DOI":"10.1109/JSTSP.2017.2784180","ISBN":"","ISSN":"1941-0484","URL":"","abstract":"","annote":"","author":[{"family":"Dörner","given":"S."},{"family":"Cammerer","given":"S."},{"family":"Hoydis","given":"J."},{"family":"ten Brink","given":"S."}],"citation-label":"learning_to_communicate","collection-editor":[],"collection-title":"","container-author":[],"container-title":"IEEE Journal of Selected Topics in Signal Processing","documents":[],"edition":"","editor":[],"event-date":{"date-parts":[["2018","Feb"]],"literal":"2018"},"event-place":"","id":"f698e236a380faddfb8f96d98b28f5bcinue","interhash":"a0e38e6df23c2f25303e9abd1d9fd60d","intrahash":"f698e236a380faddfb8f96d98b28f5bc","issue":"1","issued":{"date-parts":[["2018","Feb"]],"literal":"2018"},"keyword":"myown network;over-the-air;software-defined software-defined from:sdnr transmissions;open-source (artificial systems;block-based module;transmitter transmission;receiver implementations;deep computing;two-step nets;radio intelligence);neural libraries;continuous learning;communications learning;end-to-end rate;over-the-air synchronization;frame error transmissions;receiver deep neural software synchronization data learning;modulation;neural learning libraries;software radio implementations;off-the-shelf networks;NNs;block procedure;end-to-end radio;synchronisation;telecommunication radios;Training;Receivers;Communication networks;Hardware;Transmitters;Synchronization;Autoencoder;communication;deep receivers;software systems;Artificial","misc":{"issn":"1941-0484","doi":"10.1109/JSTSP.2017.2784180"},"note":"","number":"1","number-of-pages":"11","page":"132-143","page-first":"132","publisher":"","publisher-place":"","status":"","title":"Deep Learning Based Communication Over the Air","type":"article-journal","username":"inue","version":"","volume":"12"}}