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Parameter estimation for nonlinear sandwich system using instantaneous performance principle.

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This study introduces a novel identification algorithm for parameter estimation, focusing on transient performance. The new method enhances accuracy during initial estimation stages, outperforming existing techniques.

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Area of Science:

  • Control Systems Engineering
  • Signal Processing

Background:

  • Steady-state performance dominates parameter estimation research.
  • Quantifying instantaneous parameter estimation performance is challenging, especially in early stages.

Purpose of the Study:

  • To develop an identification algorithm addressing the transient performance of parameter estimations.
  • To improve the accuracy and convergence rate of parameter estimation during dynamic phases.

Main Methods:

  • Utilized predefined constraint technology and a high-effective filter for nonlinear sandwich system parameter estimation.
  • Developed intermediate variables to capture transient estimation error information.
  • Employed an error equivalent conversion technique for adaptive update law formulation.

Main Results:

  • Achieved improved convergence rates by prescribing error convergence boundaries.
  • Demonstrated good instantaneous performance via numerical examples and practical process results.
  • Successfully met predefined error convergence domains.

Conclusions:

  • The proposed algorithm effectively addresses the transient performance of parameter estimation.
  • The method offers superior instantaneous performance compared to existing schemes.
  • This work provides a valuable approach for real-time parameter estimation applications.