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Abstract and Applied Analysis
Volume 2014, Article ID 869879, 11 pages
Research Article

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

1School of Automation, Chongqing University, Chongqing 400044, China
2Chongqing College of Electronic Engineering, Chongqing 401331, China

Received 13 January 2014; Accepted 13 February 2014; Published 1 April 2014

Academic Editor: Peng Shi

Copyright © 2014 Xiaosuo Luo and Yongduan Song. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.