Identification test design for multivariable model-based control: An industrial perspective

The design of plant tests to generate data for identification of dynamic models is critically important for development of model-based process control systems. Multivariable process identification tests in industry continue to rely on uncorrelated input signals, even though investigations have shown...

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Bibliographic Details
Published in:Control engineering practice 2014-01, Vol.22, p.165-180
Main Authors: Darby, Mark L., Nikolaou, Michael
Format: Article
Language:English
Subjects:
Computer simulation
Constraints
Control-relevant identification
Controllability
Design engineering
Design of experiments
Dynamic models
Dynamical systems
Ill-conditioned systems
Joints
Model-based control
Multivariable
Multivariable systems
Signal processing
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Summary:The design of plant tests to generate data for identification of dynamic models is critically important for development of model-based process control systems. Multivariable process identification tests in industry continue to rely on uncorrelated input signals, even though investigations have shown the benefits of other input designs which lead to correlated, higher-amplitude input signals. This is partly due to difficulties in formulating and solving computationally tractable problems for identification test design. In this work, related results are summarized and extended. Connections between different designs that target D-optimality or integral controllability are established. Related concepts are illustrated through simulation case studies.
ISSN:0967-0661
1873-6939
DOI:10.1016/j.conengprac.2013.06.018