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- Braun, Martin W., et al.
(författare)
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A 'Model-on-Demand' Identification Methodology for Nonlinear Process Systems
- 2001
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Ingår i: International Journal of Control. - : Informa UK Limited. - 0020-7179 .- 1366-5820. ; 74:18, s. 1708-1717
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Tidskriftsartikel (refereegranskat)abstract
- An identification methodology based on multi-level pseudo-random sequence (multi-level PRS) input signals and 'Model-on-Demand' (MoD) estimation is presented for single-input, single-output non-linear process applications. 'Model-on-Demand' estimation allows for accurate prediction of non-linear systems while requiring few user choices and without solving a non-convex optimization problem, as is usually the case with global modelling techniques. By allowing the user to incorporate a priori information into the specification of design variables for multi-level PRS input signals, a sufficiently informative input-output dataset for MoD estimation is generated in a 'plant-friendly' manner. The usefulness of the methodology is demonstrated in case studies involving the identification of a simulated rapid thermal processing (RTP) reactor and a pilot-scale brine-water mixing tank. On the resulting datasets, MoD estimation displays performance comparable to that achieved via semi-physical modelling and semi-physical modelling combined with neural networks. The MoD estimator, however, achieves this level of performance with substantially lower engineering effort.
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