Masked Multiple State Space Model Identification Using FRD and Evolutionary Optimization

Identification of dynamical systems from frequency response data (FRD) has extensively been studied and effective techniques have been developed. Given different FRD sets obtained from different systems and a fixed state space model structure, is it possible to find a constant parameter vector conta...

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Bibliographic Details
Published in:IEEE transactions on industrial informatics Vol. 20; no. 7; pp. 9861 - 9869
Main Authors: Efe, Mehmet Onder, Kurkcu, Burak, Kasnakoglu, Cosku, Mohamed, Zaharuddin, Liu, Zhijie
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.07.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Costs
Dynamic models
Dynamical systems
Frequency response
Genetic algorithms
Genetic algorithms (GAs)
identification
masked models
Optimization
Parameters
Social factors
State space models
State-space methods
System effectiveness
Time-frequency analysis
ISSN:1551-3203, 1941-0050
Online Access:Get full text
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Summary:Identification of dynamical systems from frequency response data (FRD) has extensively been studied and effective techniques have been developed. Given different FRD sets obtained from different systems and a fixed state space model structure, is it possible to find a constant parameter vector containing <inline-formula><tex-math notation="LaTeX">(\mathbf {A},\mathbf {B},\mathbf {C},\mathbf {D})</tex-math></inline-formula> quadruple's numerical content and a FRD-associated mask vector set that approximates the spectral information available in each FRD set? This article proposes a genetic algorithm based optimization approach to determine the real parameter vector <inline-formula><tex-math notation="LaTeX">(\mathbf {A},\mathbf {B},\mathbf {C},\mathbf {D})</tex-math></inline-formula> and the binary mask vector through a sequential optimization scheme. We study state space models for matching FRD from multiple systems. Results show that the proposed optimization approach solves the problem and compresses multiple dynamical models into a single masked one.
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ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2024.3388605