Realistic disturbance modeling using Hidden Markov Models: Applications in model-based process control

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Understanding and modeling disturbances play a critical part in designing effective advanced model-based control solutions. Existing linear, stationary disturbance models are oftentimes limiting in the face of time-varying characteristics typically witnessed in process industries. These include intermittent drifts, abrupt changes, temporary oscillations, outliers and the likes. This work proposes a Hidden Markov Model-based framework to deal with such situations that exhibit discrete, modal behavior. The usefulness of the proposed disturbance framework - from modeling to ensuring the integral action under a wide variety of scenarios - is demonstrated through several examples. (C) 2009 Elsevier Ltd. All rights reserved.
Publisher
ELSEVIER SCI LTD
Issue Date
2009-10
Language
English
Article Type
Article
Keywords

PREDICTIVE CONTROL; LINEAR-MODELS; SYSTEMS; ALGORITHM

Citation

JOURNAL OF PROCESS CONTROL, v.19, no.9, pp.1438 - 1450

ISSN
0959-1524
DOI
10.1016/j.jprocont.2009.04.014
URI
http://hdl.handle.net/10203/101346
Appears in Collection
CBE-Journal Papers(저널논문)
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