Data filtering‐based parameter estimation algorithms for a class of nonlinear systems with colored noises

This article studies the data filtering‐based identification algorithms for a class of nonlinear system with autoregressive noise. By means of the data filtering technique and the hierarchical identification principle, the identification model is transformed into two sub‐identification models, and a...

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Bibliographic Details
Published in:Optimal control applications & methods 2023-09, Vol.44 (5), p.2338-2357
Main Authors: Zhang, Chen, Liu, Yang, Li, Li, Song, Fazhi
Format: Article
Language:English
Subjects:
Filtration
Innovations
Iterative algorithms
Iterative methods
Mathematical models
Nonlinear systems
Parameter estimation
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Summary:This article studies the data filtering‐based identification algorithms for a class of nonlinear system with autoregressive noise. By means of the data filtering technique and the hierarchical identification principle, the identification model is transformed into two sub‐identification models, and a filtering hierarchical gradient‐based iterative algorithm is proposed for improving parameter estimation accuracy and reducing computational burden. Meanwhile, to further improve the identification performance, the multi‐innovation identification theory is used to derived the filtering hierarchical multi‐innovation gradient‐based iterative algorithm. The gradient‐based iterative algorithm is given for comparison. The analysis shows that the filtering hierarchical gradient‐based iterative algorithm has smaller computational burden and can give more accurate parameter estimates than the gradient‐based iterative algorithm, and the filtering hierarchical multi‐innovation gradient‐based iterative algorithm can track time‐varying parameters based on the dynamical window data. Finally, the example part is provided to verify the effectiveness of the proposed algorithms.
ISSN:0143-2087
1099-1514
DOI:10.1002/oca.2985