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Processes 2017, 5(3), 42; doi:10.3390/pr5030042

Design of Experiments for Control-Relevant Multivariable Model Identification: An Overview of Some Basic Recent Developments

Chemical & Biomolecular Engineering Department, University of Houston, Houston, TX 77204-4004, USA
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Received: 22 May 2017 / Revised: 26 July 2017 / Accepted: 29 July 2017 / Published: 3 August 2017
(This article belongs to the Collection Process Data Analytics)
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Abstract

The effectiveness of model-based multivariable controllers depends on the quality of the model used. In addition to satisfying standard accuracy requirements for model structure and parameter estimates, a model to be used in a controller must also satisfy control-relevant requirements, such as integral controllability. Design of experiments (DOE), which produce data from which control-relevant models can be accurately estimated, may differ from standard DOE. The purpose of this paper is to emphasize this basic principle and to summarize some fundamental results obtained in recent years for DOE in two important cases: Accurate estimation of the order of a multivariable model and efficient identification of a model that satisfies integral controllability; both important for the design of robust model-based controllers. For both cases, we provide an overview of recent results that can be easily incorporated by the final user in related DOE. Computer simulations illustrate outcomes to be anticipated. Finally, opportunities for further development are discussed. View Full-Text
Keywords: design of experiments; integral controllability; subspace identification; multivariable control; model order design of experiments; integral controllability; subspace identification; multivariable control; model order
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Misra, S.; Darby, M.; Panjwani, S.; Nikolaou, M. Design of Experiments for Control-Relevant Multivariable Model Identification: An Overview of Some Basic Recent Developments. Processes 2017, 5, 42.

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