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Mahdi Kazemi1, Mohammad Mehdi Arefi1

  • 1Department of Power and Control Engineering, School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran.

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Summary

This study introduces an online identification algorithm for nonlinear systems with colored noise, utilizing an extended recursive least squares (ERLS) approach for robust parameter estimation and efficient system modeling.

Keywords:
CSTR processHighly nonlinear systemsIterative recursive algorithmLeast squares identificationParameter estimationWiener modelpH neutralization process

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Area of Science:

  • Control Systems Engineering
  • Signal Processing
  • System Identification

Background:

  • Nonlinear systems identification is challenging due to colored noise.
  • Accurate online parameter estimation is crucial for adaptive control.
  • Existing methods may lack robustness to parameter variations.

Purpose of the Study:

  • To develop an online identification algorithm for nonlinear systems with output colored noise.
  • To enhance robustness against system parameter variations.
  • To improve the efficiency and convergence rate of system identification.

Main Methods:

  • Utilizing an extended recursive least squares (ERLS) algorithm.
  • Employing a polynomial Wiener model for system representation.
  • Estimating an unknown intermediate signal via an inner iterative algorithm.
  • Incorporating a robust recursive least squares (RLS) algorithm for enhanced stability.

Main Results:

  • The proposed algorithm demonstrates fast convergence.
  • The method exhibits robust characteristics in the presence of noise and parameter variations.
  • Achieved 92% FIT criterion in a Continuous Stirred-Tank Reactor (CSTR) process with approximately 400 data points.
  • Simulation results validate the effectiveness of the online identification approach.

Conclusions:

  • The developed online identification algorithm is effective for nonlinear systems with colored noise.
  • The combination of ERLS and robust RLS provides fast convergence and strong robustness.
  • The proposed method enhances the efficiency of system identification in dynamic processes.