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Adaptive Volterra-Laguerre modeling for NMPC

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Publication date2007
Host publicationSignal Processing and Its Applications, 2007. ISSPA 2007. 9th International Symposium on
Place of PublicationNew York
PublisherIEEE
Pages1322-1325
Number of pages4
ISBN (Print)978-1-4244-0778-1
<mark>Original language</mark>English

Conference

Conference9th International Symposium on Signal Processing and its Applications
CitySharjah
Period12/02/0715/02/07

Conference

Conference9th International Symposium on Signal Processing and its Applications
CitySharjah
Period12/02/0715/02/07

Abstract

Model Predictive Control (MPC) is one of the most successful controllers in process industries. Process industries need a predictive controller that is low cost, easy to setup and maintains an adaptive behavior which accounts for plant changes, nonlinearities and under-modeling. To this aim, it is necessary to obtain a suitable adaptive modeling that can be easily used in nonlinear MPC framework. Experiments show performance advantages of Volterra series in terms of convergence, interpretability, and system sizes that can be handled They can be used to model a wide class of nonlinear systems. However, since these models are in general non-parsimonious in parameters, in this paper the symmetric kernel parameters and Laguerre filtering are used to generate regression vector. The performance of the proposed method is evaluated by simulation results obtained for identification experiments of a pH-neutralization process.