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System Identification Algorithm for Non-Uniformly Sampled Data.

Korkut Bekiroglu1, Constantino Lagoa2, Stephanie T Lanza3

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Summary
This summary is machine-generated.

This study introduces an atomic norm minimization method for identifying continuous time models from noisy, non-uniformly sampled data. The approach efficiently determines reliable, low-order models without data preprocessing.

Keywords:
Continuous time system identificationnon-uniformly sampled dataparsimonious system identificationrandomized system identification algorithm

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Area of Science:

  • Control Engineering
  • Signal Processing
  • System Identification

Background:

  • Controller design relies on accurate system models.
  • Discrete-time model identification is well-developed, but continuous-time methods are limited.
  • Existing methods struggle with noisy and non-uniformly sampled data.

Purpose of the Study:

  • To develop a robust method for identifying parsimonious continuous-time models.
  • To address limitations in current system identification techniques for real-world data.
  • To enable reliable low-order model determination from noisy, non-uniformly sampled data.

Main Methods:

  • Proposed an atomic norm minimization approach for continuous-time system identification.
  • The method handles non-uniformly sampled data directly, avoiding preprocessing.
  • Leverages convex optimization for robust model estimation.

Main Results:

  • Successfully identified parsimonious continuous-time models from simulated and academic data.
  • Demonstrated efficiency and reliability in handling noisy, non-uniformly sampled datasets.
  • The method provides a practical solution for complex system identification challenges.

Conclusions:

  • The proposed atomic norm minimization is effective for continuous-time system identification.
  • This method offers a significant advancement for modeling systems with challenging data.
  • Applicable to various fields, including engineering and behavioral studies.