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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Vydané v:International journal of adaptive control and signal processing Ročník 37; číslo 2; s. 447 - 473
Hlavný autor: Wigren, Torbjörn
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Bognor Regis Wiley Subscription Services, Inc 01.02.2023
Predmet:
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
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Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0890-6327
1099-1115
1099-1115
DOI:10.1002/acs.3531