Adaptive Predictive Control: A Data-Driven Closed-Loop Subspace Identification Approach

This paper presents a data-driven adaptive predictive control method using closed-loop subspace identification. As the predictor is the key element of the predictive controller, we propose to derive such predictor based on the subspace matrices which are obtained through the closed-loop subspace ide...

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Main Authors: Xiaosuo Luo, Yongduan Song
Format: Article
Language:English
Published: Wiley 2014-01-01
Series:Abstract and Applied Analysis
Online Access:http://dx.doi.org/10.1155/2014/869879
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author Xiaosuo Luo
Yongduan Song
author_facet Xiaosuo Luo
Yongduan Song
author_sort Xiaosuo Luo
collection DOAJ
description This paper presents a data-driven adaptive predictive control method using closed-loop subspace identification. As the predictor is the key element of the predictive controller, we propose to derive such predictor based on the subspace matrices which are obtained through the closed-loop subspace identification algorithm driven by input-output data. Taking advantage of transformational system model, the closed-loop data is effectively processed in this subspace algorithm. By combining the merits of receding window and recursive identification methods, an adaptive mechanism for online updating subspace matrices is given. Further, the data inspection strategy is introduced to eliminate the negative impact of the harmful (or useless) data on the system performance. The problems of online excitation data inaccuracy and closed-loop identification in adaptive control are well solved in the proposed method. Simulation results show the efficiency of this method.
format Article
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institution Kabale University
issn 1085-3375
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language English
publishDate 2014-01-01
publisher Wiley
record_format Article
series Abstract and Applied Analysis
spelling doaj-art-19d5d756a4c4420a9bca830426e53e5e2025-02-03T01:21:48ZengWileyAbstract and Applied Analysis1085-33751687-04092014-01-01201410.1155/2014/869879869879Adaptive Predictive Control: A Data-Driven Closed-Loop Subspace Identification ApproachXiaosuo Luo0Yongduan Song1School of Automation, Chongqing University, Chongqing 400044, ChinaSchool of Automation, Chongqing University, Chongqing 400044, ChinaThis paper presents a data-driven adaptive predictive control method using closed-loop subspace identification. As the predictor is the key element of the predictive controller, we propose to derive such predictor based on the subspace matrices which are obtained through the closed-loop subspace identification algorithm driven by input-output data. Taking advantage of transformational system model, the closed-loop data is effectively processed in this subspace algorithm. By combining the merits of receding window and recursive identification methods, an adaptive mechanism for online updating subspace matrices is given. Further, the data inspection strategy is introduced to eliminate the negative impact of the harmful (or useless) data on the system performance. The problems of online excitation data inaccuracy and closed-loop identification in adaptive control are well solved in the proposed method. Simulation results show the efficiency of this method.http://dx.doi.org/10.1155/2014/869879
spellingShingle Xiaosuo Luo
Yongduan Song
Adaptive Predictive Control: A Data-Driven Closed-Loop Subspace Identification Approach
Abstract and Applied Analysis
title Adaptive Predictive Control: A Data-Driven Closed-Loop Subspace Identification Approach
title_full Adaptive Predictive Control: A Data-Driven Closed-Loop Subspace Identification Approach
title_fullStr Adaptive Predictive Control: A Data-Driven Closed-Loop Subspace Identification Approach
title_full_unstemmed Adaptive Predictive Control: A Data-Driven Closed-Loop Subspace Identification Approach
title_short Adaptive Predictive Control: A Data-Driven Closed-Loop Subspace Identification Approach
title_sort adaptive predictive control a data driven closed loop subspace identification approach
url http://dx.doi.org/10.1155/2014/869879
work_keys_str_mv AT xiaosuoluo adaptivepredictivecontroladatadrivenclosedloopsubspaceidentificationapproach
AT yongduansong adaptivepredictivecontroladatadrivenclosedloopsubspaceidentificationapproach