Scopus - Lecture Notes in Control and Information Sciences: Subspace predictive control applied to fault-tolerant control


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Lecture Notes in Control and Information Sciences
Volume 399, 2010, Pages 293-317

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ISSN: 01708643
ISBN: 978-364211689-6
DOI: 10.1007/978-3-642-11690-2_10
Document Type: Article
Source Type: Book series
Volume Editors: Edwards C, Lombaerts T, Smaili H

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Subspace predictive control applied to fault-tolerant control

Hallouzi, R.^a , Verhaegen, M.^b

^aReliaCon, Rotterdamseweg 145, Delft 2628AL, Netherlands
^bDelft Center for Systems and Control, Delft University of Technology, Mekelweg 2, Delft 2628CD, Netherlands

Abstract

Subspace identification is a technique that can be used for identification of state-space models from input-output data. This technique has drawn considerable interest in the last two decades [1, 2], especially for linear time-invariant systems. A reason for this is the efficient way in which models are identified for systems of high order and with multiple inputs and outputs. Subspace identification can be used to form a subspace predictor for prediction of future outputs from past input-output data and a future input-sequence. This subspace predictor can be computed without realization of the actual state-space models, which significantly reduces computational requirements. In [3] the subspace predictor has been combined with model predictive control [4], resulting in a control algorithm that has been given the name subspace predictive control (SPC). In SPC, the output predicted by the subspace predictor is part of the cost function of the predictive controller. As a result of the subspace predictor being generated completely from input-output data, the SPC algorithm is a data-driven one. © 2010 Springer-Verlag Berlin Heidelberg.

Language of Original Document

English

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Lecture Notes in Control and Information Sciences
Volume 399, 2010, Pages 293-317


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