Initialization of a Nonlinear Identification Algorithm Applied to Laboratory Plant Data

New techniques for recursive identification of systems described by nonlinear ordinary differential equation models are discussed. The model is of black-box state space type, where the right-hand side function is estimated as a multi-variate polynomial in the states and inputs, with the parameters s...

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Vydáno v:	IEEE transactions on control systems technology Ročník 16; číslo 4; s. 708 - 716
Hlavní autoři:	Brus, L., Wigren, T., Carlsson, B.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York, NY IEEE 01.07.2008 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Algorithms Applied sciences Approximation Automatic control Computer science; control theory; systems Control theory. Systems Differential equations Differentiation Exact sciences and technology Filtering algorithms Information technology Informationsteknik initialization Iterative algorithms Kalman filtering Kalman filters Laboratories Mathematical analysis Mathematical models Modelling and identification Nonlinear systems Nonlinearity Polynomials Predictive models Recursive recursive identification recursive prediction error method (RPEM) Reglerteknik State estimation State-space methods Studies Systems engineering Systemteknik Tanks TECHNOLOGY TEKNIKVETENSKAP Non linear estimation Differential equation Kalman filter recursive prediction error method (RPEM) Cascade control Non linear control Black box State space recursive identification State space method Recursive algorithm Modeling nonlinear systems Non linear model Recursive method initialization Kalman filtering System identification Differentiation
ISSN:	1063-6536, 1558-0865, 1558-0865
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Popis
Shrnutí:	New techniques for recursive identification of systems described by nonlinear ordinary differential equation models are discussed. The model is of black-box state space type, where the right-hand side function is estimated as a multi-variate polynomial in the states and inputs, with the parameters selected to be the polynomial coefficients. An algorithm based on Kalman filtering techniques is derived, where a numerical differentiation scheme, used for generation of approximate state variables is a key ingredient. The Kalman-filter-based algorithm is, for example, suitable for initialization of a previously published recursive prediction error method (RPEM) based on the same model. In this brief, the algorithm performance of the Kalman-filter-based method is compared to that of the RPEM using a numerical example. Another example shows that the success rate of the RPEM is increased from 70% to 100%, when the proposed algorithm is used for generation of initial estimates for the RPEM. The Kalman-filter-based algorithm is also used for finding initial parameters for the RPEM when applied to live data from a laboratory process - a system of cascaded tanks. Based on the experimental results, this brief discusses advantages and disadvantages of different algorithms and differentiation schemes.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Article-2 ObjectType-Feature-1 content type line 23
ISSN:	1063-6536 1558-0865 1558-0865
DOI:	10.1109/TCST.2007.916300