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Identification of MIMO State Space Model based on MISO High-order ARX Model: Design and Application  

Won, Wangyun (Department of Chemical and Biomolecular Engineering, Sogang University)
Yoon, Jieun (Department of Chemical and Biomolecular Engineering, Sogang University)
Lee, Kwang Soon (Department of Chemical and Biomolecular Engineering, Sogang University)
Lee, Bongkook (LS Industrial System Co., Ltd.)
Publication Information
Korean Chemical Engineering Research / v.45, no.1, 2007 , pp. 67-72 More about this Journal
Abstract
An efficient method for identification of MIMO state space model has been developed by combining partial least squares (PLS) regression, balanced realization, and balanced truncation. In the developed method, a MIMO system is decomposed into multiple MISO systems each of which is represented by a high-order ARX model and the parameters of the ARX models are estimated by PLS. Then, MISO state space models for respective MISO ARX transfer function are found through realization and combined to a MIMO state space model. Finally, a minimal balanced MIMO state space model is obtained through balanced realization and truncation. The proposed method was applied to the design of model predictive control for temperature control of a high pressure $CO_2$ solubility measurement system.
Keywords
Identification; PLS; Balanced Realization; Balanced Truncation; Model Predictive Control;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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