Hierarchical Gradient-Based Iterative Parameter Estimation Algorithms for a Nonlinear Feedback System Based on the Hierarchical Identification Principle

This paper focuses on iterative parameter estimation methods for a nonlinear closed-loop system (i.e., a nonlinear feedback system) with an equation-error model for the open-loop part. By applying negative gradient search, a gradient-based iterative algorithm is constructed. To reduce the computatio...

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Veröffentlicht in:Circuits, systems, and signal processing Jg. 43; H. 1; S. 124 - 151
Hauptverfasser: Yang, Dan, Liu, Yanjun, Ding, Feng, Yang, Erfu
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.01.2024
Springer Nature B.V
Schlagworte:
Accuracy
Algorithms
Circuits and Systems
Closed loops
Decomposition
Electrical Engineering
Electronics and Microelectronics
Engineering
Feedback control
Identification
Instrumentation
Iterative algorithms
Iterative methods
Nonlinear feedback
Parameter estimation
Parameter identification
Principles
Process controls
Signal processing
Signal,Image and Speech Processing
Gradient search
Parameter estimation
Iterative identification
Nonlinear feedback system
Hierarchical identification
ISSN:0278-081X, 1531-5878
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Zusammenfassung:This paper focuses on iterative parameter estimation methods for a nonlinear closed-loop system (i.e., a nonlinear feedback system) with an equation-error model for the open-loop part. By applying negative gradient search, a gradient-based iterative algorithm is constructed. To reduce the computational costs and improve the parameter estimation accuracy, the hierarchical identification principle is employed to derive a hierarchical gradient-based iterative algorithm. A simulation example is provided to test the effectiveness of the proposed algorithms.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:0278-081X
1531-5878
DOI:10.1007/s00034-023-02477-1