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Static gain estimation for nonlinear dynamic systems from steady-state values hidden in historical data.

Jiandong Wang1, Mengyao Wei1, Xiaotong Xing1

  • 1College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, Shandong Province, China.

ISA Transactions
|March 22, 2021
PubMed
Summary

This study introduces a novel method to estimate static gains for nonlinear dynamic systems using historical data. The approach identifies steady-state values and applies linear regression for accurate gain estimation in various operating conditions.

Keywords:
Linear regressionNonlinear dynamic systemsPiece-wise linear representationStatic gainsSteady state valuesSystem identification

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Area of Science:

  • Control Systems Engineering
  • Nonlinear Dynamics
  • System Identification

Background:

  • Static gains are crucial for controlling, diagnosing, and optimizing nonlinear dynamic systems.
  • Estimating these gains from historical data, especially from steady-state conditions, presents a significant challenge.
  • Existing methods may lack practicality or simplicity in real-world applications.

Purpose of the Study:

  • To develop a new, practical approach for estimating static gains of nonlinear dynamic systems.
  • To utilize steady-state values automatically extracted from historical operational data.
  • To provide a method that is both verifiable and easy to implement.

Main Methods:

  • Automatic extraction of steady-state values from historical data by identifying stable operating segments.
  • Estimation of static gains using linear regression applied to the extracted input-output steady-state values.
  • Verification of extracted steady-state data and estimated static gains through visualization and comparison.

Main Results:

  • Successfully developed and demonstrated an automated method for extracting steady-state values from system data.
  • Achieved accurate estimation of static gains for nonlinear dynamic systems across different operating conditions.
  • Validated the approach's effectiveness through numerical simulations and an industrial case study.

Conclusions:

  • The proposed approach offers a simple, understandable, and implementable method for static gain estimation in nonlinear systems.
  • The technique's practical features, including verification of data and estimates, enhance its reliability.
  • The method proves effective for real-world applications, aiding in system control, diagnosis, and optimization.