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Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
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Recursive hierarchical parametric identification of Wiener-Hammerstein systems based on initial value optimization.

Qiangya Li1, Tao Liu1, Jing Na2

  • 1Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education, Dalian University of Technology, Dalian 116024, China; School of Control Science and Engineering, Dalian University of Technology, Dalian 116024, China.

ISA Transactions
|January 27, 2025
PubMed
Summary
This summary is machine-generated.

A new recursive method accurately identifies parameters in Wiener-Hammerstein systems with noise. This approach optimizes initial values using particle swarm optimization (PSO) for improved system identification.

Keywords:
Adaptive forgetting factorsAuxiliary modelInitial value optimizationRecursive hierarchical least-squaresWiener-Hammerstein system

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Area of Science:

  • Control Systems Engineering
  • Signal Processing
  • System Identification

Background:

  • Wiener-Hammerstein systems are widely used but challenging to identify due to their nonlinear structure and stochastic noise.
  • Traditional recursive identification methods often suffer from initial value sensitivity and parameter cross-coupling.

Purpose of the Study:

  • To propose a novel recursive hierarchical parametric identification method for Wiener-Hammerstein systems.
  • To address challenges of stochastic noise, parameter cross-coupling, and initial value sensitivity in system identification.

Main Methods:

  • A generalized Wiener-Hammerstein model formulation for unique parameter expression.
  • A hierarchical identification algorithm separating coupled and uncoupled parameters.
  • An auxiliary block model for predicting internal variables and ensuring consistent estimation.
  • Adaptive forgetting factors to enhance convergence rates.
  • Particle Swarm Optimization (PSO) for initial value optimization.

Main Results:

  • The proposed method successfully identifies parameters in Wiener-Hammerstein systems with stochastic noise.
  • Hierarchical identification effectively avoids parameter cross-coupling.
  • PSO-based initial value optimization mitigates sensitivity issues.
  • Experimental validation on a micro-positioning stage confirms the method's efficacy.

Conclusions:

  • The novel recursive hierarchical method offers accurate and robust parameter identification for Wiener-Hammerstein systems.
  • The approach enhances convergence and overcomes limitations of traditional methods.
  • The validated method shows significant potential for practical applications in system control and analysis.