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Adaptive prescribed performance estimator for Hammerstein-like system identification based on quantized observations

Huijie Lei1, Yanwei Zhang2, Xikun Lu3

  • 1School of Electronic Information and Electrical Engineering, Anyang Institute of Technology, Anyang, 455000, People's Republic of China. anyang1109@163.com.

Scientific Reports
|December 31, 2024
PubMed
Summary

This study introduces adaptive prescribed performance for identifying parameters in Hammerstein-like systems with quantized data. It improves transient performance in parameter estimation, addressing a gap in system identification research.

Keywords:
Hammerstein-likeParameter estimationPrescribed performance technologyQuantized observationsRecursive identification

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Area of Science:

  • Control Systems Engineering
  • System Identification
  • Adaptive Control

Background:

  • Prescribed performance technology offers quantitative insights into control system steady-state and transient behaviors.
  • Improved transient performance in parameter estimation simplifies controller design and reduces system regulation time.
  • Limited research exists on transient performance in parameter identification due to challenges in error variable design.

Purpose of the Study:

  • To develop an adaptive prescribed performance approach for parameter identification in Hammerstein-like systems with quantized observations.
  • To address the difficulty in designing error variables for transient performance in parameter identification.
  • To enhance the transient performance of parameter estimation for improved control system design.

Main Methods:

  • Integration of prescribed performance technology into estimator design.
  • Development of a low-pass filter and forcing variables to define transient performance error.
  • Introduction of an improved prescribed performance function for parameter estimation error bounds.
  • Utilizing identification error transformation to reformulate the system and avoid constraints.
  • Proposing a novel adaptive law for guaranteed prescribed performance parameter identification.

Main Results:

  • Successfully integrated prescribed performance into parameter identification for Hammerstein-like systems.
  • Developed a method to characterize and bound parameter estimation errors using an improved prescribed performance function.
  • Demonstrated the effectiveness of the proposed adaptive law in achieving prescribed performance.
  • Validated the approach through simulation and process examples.

Conclusions:

  • The proposed method effectively addresses the challenge of transient performance in parameter identification for quantized Hammerstein-like systems.
  • The integration of prescribed performance technology offers a viable solution for improving parameter estimation accuracy and transient behavior.
  • The findings contribute to advancing system identification techniques, particularly for complex nonlinear systems.