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

IF 0.4 4区 计算机科学 Q4 AUTOMATION & CONTROL SYSTEMS
Zeineb Lassoued, Kamel Abderrahim
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引用次数: 0

摘要

研究了基于聚类的多输入多输出分段仿射系统辨识问题。该方法最初是为具有多输入单输出(MISO)结构的系统设计的,通过三个主要步骤进行,即数据聚类,参数向量估计和区域计算。数据聚类是最重要的步骤,因为其他两个步骤取决于所使用的聚类算法给出的结果。对于MIMO PWA系统,我们应该对被认为是高维数据的参数矩阵进行聚类。然而,大多数传统聚类算法的有效性和效率都不高,因为基于对象之间距离的相似性评估在高维空间中效果不佳。因此,我们提出了一种扩展的DBSCAN(基于密度的空间聚类应用与噪声)聚类方法来识别MIMO PWA系统。仿真结果证明了该方法的有效性。最后将该方法应用于一个工业机器人机械手上,验证了仿真结果。DOI: 10.61416 / ceai.v25i2.8523
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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
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来源期刊
CiteScore
1.50
自引率
22.20%
发文量
0
审稿时长
6 months
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.
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