Recursive identification of a nonlinear state space model

Summary The convergence of a recursive prediction error method is analyzed. The algorithm identifies a nonlinear continuous time state space model, parameterized by one right‐hand side component of the differential equation and an output equation with a fixed differential gain, to avoid over‐paramet...

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Bibliographic Details
Published in:International journal of adaptive control and signal processing Vol. 37; no. 2; pp. 447 - 473
Main Author: Wigren, Torbjörn
Format: Journal Article
Language:English
Published: Bognor Regis Wiley Subscription Services, Inc 01.02.2023
Subjects:
Algorithms
Automatic Control
averaging
Convergence
Criteria
Differential equations
Error analysis
Nonlinear dynamics
nonlinear systems
Parameterization
Power plants
prediction error method
Reglerteknik
Stability analysis
State space models
state-space model
ISSN:0890-6327, 1099-1115, 1099-1115
Online Access:Get full text
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Summary:Summary The convergence of a recursive prediction error method is analyzed. The algorithm identifies a nonlinear continuous time state space model, parameterized by one right‐hand side component of the differential equation and an output equation with a fixed differential gain, to avoid over‐parametrization. The method minimizes the criterion by simulation using an Euler discretization. A stability analysis of the associated differential equations results in conditions for (local) convergence to a minimum of the criterion function. Simulations verify the theoretical analysis and illustrate the performance in the presence of unmodeled dynamics, by identification of the nonlinear drum boiler dynamics of a power plant model.
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content type line 14
ISSN:0890-6327
1099-1115
1099-1115
DOI:10.1002/acs.3531