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Recursive parameter estimation for Hammerstein-Wiener systems using modified EKF algorithm.

Feng Yu1, Zhizhong Mao1, Ping Yuan1

  • 1Department of Control Theory and Control Engineering, Northeastern University, No.11, Lane 3, WenHua Road, HePing District, Shenyang, China.

ISA Transactions
|June 15, 2017
PubMed
Summary
This summary is machine-generated.

This study presents new recursive algorithms for estimating parameters in Hammerstein-Wiener systems. A modified algorithm expands the convergence domain for improved recursive parameter estimation in complex systems.

Keywords:
Extended Kalman filter algorithmHammerstein-Wiener systemMultiple input single outputRecursive parameter estimationUniform convergence

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Area of Science:

  • Control Systems Engineering
  • Signal Processing
  • System Identification

Background:

  • Hammerstein-Wiener models are widely used for nonlinear system representation.
  • Recursive parameter estimation is crucial for real-time control and adaptation.
  • Existing methods for multiple input single output (MISO) Hammerstein-Wiener systems are limited.

Purpose of the Study:

  • To develop novel recursive parameter estimation algorithms for single input single output (SISO) and multiple input single output (MISO) Hammerstein-Wiener systems.
  • To address the limitations of small parameter convergence domains in existing algorithms.
  • To propose a modified algorithm that enhances the convergence properties.

Main Methods:

  • Derivation of two basic recursive algorithms inspired by the extended Kalman filter (EKF).
  • Utilizing first and second-order Taylor approximations for algorithm development.
  • Proposing a modified algorithm based on the first-order approximation to improve convergence domain.

Main Results:

  • Two basic recursive algorithms were successfully derived for Hammerstein-Wiener systems.
  • A modified algorithm demonstrated an expanded parameter convergence domain compared to the first-order approach.
  • The proposed methods showed validity through convergence analysis and simulation case studies.

Conclusions:

  • The developed recursive algorithms provide effective methods for parameter estimation in Hammerstein-Wiener systems.
  • The modified algorithm offers a significant improvement in convergence domain, overcoming limitations of prior methods.
  • The study contributes practical solutions for identifying complex nonlinear systems.