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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Bibliographic Details
Published in:IEEE transactions on control systems technology Vol. 16; no. 4; pp. 708 - 716
Main Authors: Brus, L., Wigren, T., Carlsson, B.
Format: Journal Article
Language:English
Published: New York, NY IEEE 01.07.2008
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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
Online Access:Get full text
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Description
Summary: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.
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ISSN:1063-6536
1558-0865
1558-0865
DOI:10.1109/TCST.2007.916300