{"title":"多输入多输出片仿射系统中基于噪声的密度空间聚类应用的聚类识别扩展:在工业机器人上的应用","authors":"Zeineb Lassoued, Kamel Abderrahim","doi":"10.61416/ceai.v25i2.8523","DOIUrl":null,"url":null,"abstract":"In this paper the problem of clustering based identificationof a Multi-Input Multi-Output (MIMO) PieceWise Affinesystems (PWA) is considered. This approach, originallydesigned for systems with a Multiple-Input Single-Output(MISO) structure, is carried out by three main steps whichare data clustering, parameters vectors estimation and regionscomputing. Data clustering is the most important stepbecause the two other steps depend on the results given bythe used clustering algorithm. In case of MIMO PWA systems,we should cluster matrices of parametres which areconsidered high dimensionnal data. However, most of theconventional clustering algorithms do not work well in termsof effectiveness and efficiency since the similarity assessmentwhich is based on the distances between objects is fruitlessin high dimension space. Therefore, we propose an extensionof the DBSCAN (Density Based Spatial Clusteringof Applications with Noise) clustering approach for the identificationof MIMO PWA systems. The simulation resultspresented in this paper prouve the performance of the suggestedapproach. An application of the proposed approachto an industrial robot manipulator is also presented in orderto validate the simulation results. DOI: 10.61416/ceai.v25i2.8523","PeriodicalId":50616,"journal":{"name":"Control Engineering and Applied Informatics","volume":null,"pages":null},"PeriodicalIF":0.4000,"publicationDate":"2023-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Extension of the clustering identification by extending the Density Based Spatial Clustering of Applications with Noise approach to Multi-Input Multi-Output Piece Wise Affine systems: Application to an industrial robot\",\"authors\":\"Zeineb Lassoued, Kamel Abderrahim\",\"doi\":\"10.61416/ceai.v25i2.8523\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper the problem of clustering based identificationof a Multi-Input Multi-Output (MIMO) PieceWise Affinesystems (PWA) is considered. This approach, originallydesigned for systems with a Multiple-Input Single-Output(MISO) structure, is carried out by three main steps whichare data clustering, parameters vectors estimation and regionscomputing. Data clustering is the most important stepbecause the two other steps depend on the results given bythe used clustering algorithm. In case of MIMO PWA systems,we should cluster matrices of parametres which areconsidered high dimensionnal data. However, most of theconventional clustering algorithms do not work well in termsof effectiveness and efficiency since the similarity assessmentwhich is based on the distances between objects is fruitlessin high dimension space. Therefore, we propose an extensionof the DBSCAN (Density Based Spatial Clusteringof Applications with Noise) clustering approach for the identificationof MIMO PWA systems. The simulation resultspresented in this paper prouve the performance of the suggestedapproach. An application of the proposed approachto an industrial robot manipulator is also presented in orderto validate the simulation results. DOI: 10.61416/ceai.v25i2.8523\",\"PeriodicalId\":50616,\"journal\":{\"name\":\"Control Engineering and Applied Informatics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.4000,\"publicationDate\":\"2023-06-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Control Engineering and Applied Informatics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.61416/ceai.v25i2.8523\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Control Engineering and Applied Informatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.61416/ceai.v25i2.8523","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
Extension of the clustering identification by extending the Density Based Spatial Clustering of Applications with Noise approach to Multi-Input Multi-Output Piece Wise Affine systems: Application to an industrial robot
In this paper the problem of clustering based identificationof a Multi-Input Multi-Output (MIMO) PieceWise Affinesystems (PWA) is considered. This approach, originallydesigned for systems with a Multiple-Input Single-Output(MISO) structure, is carried out by three main steps whichare data clustering, parameters vectors estimation and regionscomputing. Data clustering is the most important stepbecause the two other steps depend on the results given bythe used clustering algorithm. In case of MIMO PWA systems,we should cluster matrices of parametres which areconsidered high dimensionnal data. However, most of theconventional clustering algorithms do not work well in termsof effectiveness and efficiency since the similarity assessmentwhich is based on the distances between objects is fruitlessin high dimension space. Therefore, we propose an extensionof the DBSCAN (Density Based Spatial Clusteringof Applications with Noise) clustering approach for the identificationof MIMO PWA systems. The simulation resultspresented in this paper prouve the performance of the suggestedapproach. An application of the proposed approachto an industrial robot manipulator is also presented in orderto validate the simulation results. DOI: 10.61416/ceai.v25i2.8523
期刊介绍:
The Journal is promoting theoretical and practical results in a large research field of Control Engineering and Technical Informatics. It has been published since 1999 under the Romanian Society of Control Engineering and Technical Informatics coordination, in its quality of IFAC Romanian National Member Organization and it appears quarterly.
Each issue has up to 12 papers from various areas such as control theory, computer engineering, and applied informatics. Basic topics included in our Journal since 1999 have been time-invariant control systems, including robustness, stability, time delay aspects; advanced control strategies, including adaptive, predictive, nonlinear, intelligent, multi-model techniques; intelligent control techniques such as fuzzy, neural, genetic algorithms, and expert systems; and discrete event and hybrid systems, networks and embedded systems. Application areas covered have been environmental engineering, power systems, biomedical engineering, industrial and mobile robotics, and manufacturing.