Hierarchical gradient-based iterative parameter estimation algorithms for a nonlinear feedback system based on the hierarchical identification principle

Dan Yang, Yanjun Liu, Feng Ding*, Erfu Yang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

60 Citations (Scopus)
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Abstract

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.

Original languageEnglish
Pages (from-to)124-151
Number of pages28
JournalCircuits, Systems, and Signal Processing
Volume43
Issue number1
Early online date17 Aug 2023
DOIs
Publication statusPublished - 1 Jan 2024

Funding

This work was supported by the National Natural Science Foundation of China (No. 62273167, 62076110), the 111 Project (B23008), the Natural Science Foundation of Shanghai (No. 22ZR1445300) and the Academic Research Fund (Applied Technology) Project (CFK201808) of Changzhou Vocational Institute of Textile and Garment and the Fundamental Research Funds for the Central Universities under Grant (JUSRP221027).

Keywords

  • Gradient search
  • Hierarchical identification
  • Iterative identification
  • Nonlinear feedback system
  • Parameter estimation

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