NDLI: Controller performance assessment for switched linear systems via applying non-negative tensor factorization

Content Provider	IEEE Xplore Digital Library
Author	Deng-Yin Jiang Li-Sheng Hu
Copyright Year	2015
Description	Author affiliation: Dept. of Autom., Shanghai Jiao Tong Univ., Shanghai, China (Deng-Yin Jiang; Li-Sheng Hu)
Abstract	This paper considers the controller performance assessment problem based on the data-driven benchmark by applying the tensor approach for switched systems. For each subprocess, the multivariate output with finite sample numbers is extracted regarding as the forms of feature matrices. Consequently, by stacking the feature matrices associated to any data set samplings, a tensor is created, a fact which necessitates studying the controller performance assessment for switched systems considered using tensors. In order to fit the multiple invariance of the measurement output tensor data processing for controller performance assessment, the non-negative tensor factorization model is proposed to analyze the tensor data, which stems from uniqueness of low-rank decomposition of higher-order tensor. By using the non-negative tensor factorization, the estimated latent outputs structure combining with the covariance-based algorithm are given to derive the data-driven performance benchmark and performance index for each subprocess controller, and the presented data-driven benchmark estimation algorithm requires a set of close-loop routine operating data. A simulation example is provided to test the effectiveness and advantages of proposed method.
Starting Page	2194
Ending Page	2199
File Size	190660
Page Count	6
File Format	PDF
ISBN	9789881563897
ISSN	19341768
DOI	10.1109/ChiCC.2015.7259974
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-07-28
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Technical Committee on Control Theory, Chinese Association of Automation
Subject Keyword	Switched linear systems Tensile stress Tensor Process control Switches Data-driven benchmark Benchmark testing Non-negative tensor factorization Controller performance assessment Matrix decomposition Covariance matrices
Content Type	Text
Resource Type	Article

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