Pub Date : 2020-09-14DOI: 10.1109/AMC44022.2020.9244347
N. Mooren, G. Witvoet, Ibrahim Açan, Joep Kooijman, T. Oomen
Positioning systems are often subject to position-domain disturbances: these are periodic in the position domain yet can appear a-periodic in the time domain. The aim of this paper is to develop a position-domain repetitive control approach that allows to attenuate disturbances with arbitrary varying period. The key idea is to implement a memory loop in the position domain on the basis of non-equidistantly distributed observations, which are inherent to the position domain. An experimental validation on an industrial substrate carrier shows a major performance improvement for a large range of velocities.
{"title":"Suppressing Position-Dependent Disturbances in Repetitive Control: With Application to a Substrate Carrier System","authors":"N. Mooren, G. Witvoet, Ibrahim Açan, Joep Kooijman, T. Oomen","doi":"10.1109/AMC44022.2020.9244347","DOIUrl":"https://doi.org/10.1109/AMC44022.2020.9244347","url":null,"abstract":"Positioning systems are often subject to position-domain disturbances: these are periodic in the position domain yet can appear a-periodic in the time domain. The aim of this paper is to develop a position-domain repetitive control approach that allows to attenuate disturbances with arbitrary varying period. The key idea is to implement a memory loop in the position domain on the basis of non-equidistantly distributed observations, which are inherent to the position domain. An experimental validation on an industrial substrate carrier shows a major performance improvement for a large range of velocities.","PeriodicalId":427681,"journal":{"name":"2020 IEEE 16th International Workshop on Advanced Motion Control (AMC)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116757029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-06-18DOI: 10.1109/AMC44022.2020.9244317
Enzo Evers, R. Voorhoeve, T. Oomen
A key step in control of precision mechatronic systems is Frequency Response Function (FRF) identification. The aim of this paper is to illustrate relevant developments and solutions for FRF identification for advanced motion control. Specifically dealing with transient and/or closed-loop conditions that can normally lead to inaccurate estimation results. This yields essential insights for FRF identification for advanced motion control that are illustrated through a simulation study and validated on an experimental setup.
{"title":"On Frequency Response Function Identification for Advanced Motion Control","authors":"Enzo Evers, R. Voorhoeve, T. Oomen","doi":"10.1109/AMC44022.2020.9244317","DOIUrl":"https://doi.org/10.1109/AMC44022.2020.9244317","url":null,"abstract":"A key step in control of precision mechatronic systems is Frequency Response Function (FRF) identification. The aim of this paper is to illustrate relevant developments and solutions for FRF identification for advanced motion control. Specifically dealing with transient and/or closed-loop conditions that can normally lead to inaccurate estimation results. This yields essential insights for FRF identification for advanced motion control that are illustrated through a simulation study and validated on an experimental setup.","PeriodicalId":427681,"journal":{"name":"2020 IEEE 16th International Workshop on Advanced Motion Control (AMC)","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129285078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-04-23DOI: 10.1109/AMC44022.2020.9244341
T. Oomen
Iterative Learning Control (ILC) can achieve perfect tracking performance for mechatronic systems. The aim of this paper is to present an ILC design tutorial for industrial mechatronic systems. First, a preliminary analysis reveals the potential performance improvement of ILC prior to its actual implementation. Second, a frequency domain approach is presented, where fast learning is achieved through noncausal model inversion, and safe and robust learning is achieved by employing a contraction mapping theorem in conjunction with nonparametric frequency response functions. The approach is demonstrated on a desktop printer. Finally, a detailed analysis of industrial motion systems leads to several shortcomings that obstruct the widespread implementation of ILC algorithms. An overview of recently developed algorithms, including extensions using machine learning algorithms, is outlined that are aimed to facilitate broad industrial deployment.
{"title":"Learning for Advanced Motion Control","authors":"T. Oomen","doi":"10.1109/AMC44022.2020.9244341","DOIUrl":"https://doi.org/10.1109/AMC44022.2020.9244341","url":null,"abstract":"Iterative Learning Control (ILC) can achieve perfect tracking performance for mechatronic systems. The aim of this paper is to present an ILC design tutorial for industrial mechatronic systems. First, a preliminary analysis reveals the potential performance improvement of ILC prior to its actual implementation. Second, a frequency domain approach is presented, where fast learning is achieved through noncausal model inversion, and safe and robust learning is achieved by employing a contraction mapping theorem in conjunction with nonparametric frequency response functions. The approach is demonstrated on a desktop printer. Finally, a detailed analysis of industrial motion systems leads to several shortcomings that obstruct the widespread implementation of ILC algorithms. An overview of recently developed algorithms, including extensions using machine learning algorithms, is outlined that are aimed to facilitate broad industrial deployment.","PeriodicalId":427681,"journal":{"name":"2020 IEEE 16th International Workshop on Advanced Motion Control (AMC)","volume":"62 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120865268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1109/iecon.2018.8591223
{"title":"Intelligent Sensing Applications for Human Assistive Systems","authors":"","doi":"10.1109/iecon.2018.8591223","DOIUrl":"https://doi.org/10.1109/iecon.2018.8591223","url":null,"abstract":"","PeriodicalId":427681,"journal":{"name":"2020 IEEE 16th International Workshop on Advanced Motion Control (AMC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114478882","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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