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Time-delay estimation for SISO systems using SWσ.

Xuguang Wang1, Jie Su2, Lifeng Zhang2

  • 1Hebei Engineering Research Center of Simulation & Optimized Control for Power Generation, Baoding 071003, China; School of Control and Computer Engineering, North China Electric Power University, Baoding 071003, China.

ISA Transactions
|August 14, 2018
PubMed
Summary
This summary is machine-generated.

This study estimates time-delay in single-input single-output (SISO) systems using input-output data. It leverages a copula theory dependence measure (SWσ) to accurately determine time-delay without needing other system parameters.

Keywords:
CopulaDependence measureSWσTime-delay estimation

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Area of Science:

  • Control Systems Engineering
  • Signal Processing
  • Statistical Modeling

Background:

  • Accurate time-delay estimation is crucial for control system performance.
  • Existing methods often require prior knowledge of system parameters, limiting their applicability.
  • Uniformly sampled input-output data is readily available in many industrial applications.

Purpose of the Study:

  • To develop a novel method for time-delay estimation in SISO systems.
  • To utilize Schweizer and Wolff's sigma (SWσ) dependence measure from copula theory for this estimation.
  • To achieve time-delay estimation without estimating other system parameters.

Main Methods:

  • Employing Schweizer and Wolff's sigma (SWσ) as a dependence measure for input-output data.
  • Analyzing the quantitative relationship between time-delay and SWσ.
  • Identifying the time-delay by finding the maximum SWσ value after time-delay removal.

Main Results:

  • A direct method for time-delay estimation was successfully developed.
  • The SWσ measure effectively quantifies the dependence related to time-delay.
  • The proposed method demonstrated feasibility and effectiveness through experiments and comparisons.

Conclusions:

  • The novel SWσ-based method provides an effective approach for time-delay estimation in SISO systems.
  • This method eliminates the need for estimating other system parameters, simplifying the process.
  • The technique is validated by experimental results, showing its practical applicability.