Adaptive Predictive Control: A Data-Driven Closed-Loop Subspace Identification Approach
Joint Authors
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-04-01
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Luo, Xiaosuo& Song, Yongduan. 2014. Adaptive Predictive Control: A Data-Driven Closed-Loop Subspace Identification Approach. Abstract and Applied Analysis،Vol. 2014, no. 2014, pp.1-11.
https://search.emarefa.net/detail/BIM-1014972
Modern Language Association (MLA)
Luo, Xiaosuo& Song, Yongduan. Adaptive Predictive Control: A Data-Driven Closed-Loop Subspace Identification Approach. Abstract and Applied Analysis No. 2014 (2014), pp.1-11.
https://search.emarefa.net/detail/BIM-1014972
American Medical Association (AMA)
Luo, Xiaosuo& Song, Yongduan. Adaptive Predictive Control: A Data-Driven Closed-Loop Subspace Identification Approach. Abstract and Applied Analysis. 2014. Vol. 2014, no. 2014, pp.1-11.
https://search.emarefa.net/detail/BIM-1014972
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1014972