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Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame.
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Updated: Nov 5, 2025

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
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Closed-loop time-varying continuous-time recursive subspace-based prediction via principle angles rotation.

Miao Yu1, Ge Guo2, Jianchang Liu3

  • 1School of Control Engineering, Northeastern University at Qinhuangdao, Qinhuangdao, Hebei, China.

ISA Transactions
|May 16, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel recursive subspace method for predicting time-varying systems operating in closed-loop. It accurately estimates system matrices, overcoming identification challenges in feedback-controlled environments.

Keywords:
Closed-loop identificationGeneralized Poisson moment functionalsPrincipal component analysisPrinciple angles rotationSubspace identificationSubspace prediction

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Area of Science:

  • Systems and Control Engineering
  • Signal Processing
  • Fault Detection and Diagnosis

Background:

  • Accurate system identification is crucial for control and monitoring, especially in closed-loop systems.
  • Traditional methods struggle with biased results due to feedback controllers.
  • Subspace-based methods offer a promising alternative for system identification.

Purpose of the Study:

  • To develop a closed-loop, time-varying, continuous-time recursive subspace-based prediction method.
  • To address the challenges of biased system identification in feedback-controlled systems.
  • To improve the accuracy and robustness of system matrix estimation.

Main Methods:

  • Utilizing principle angles rotation for subspace identification.
  • Employing generalized Poisson moment functionals for linear mapping and handling time-derivatives.
  • Adopting parity space instead of observable subspace for fault detection.
  • Consistent estimation of system matrices via instrumental variable method and principal component analysis.

Main Results:

  • The proposed method consistently estimates system matrices, resolving biased identification issues.
  • Principle angles rotation effectively predicts system matrices from signal subspaces.
  • Numerical simulations and real-world applications demonstrate the method's effectiveness.

Conclusions:

  • The developed recursive subspace-based prediction method is effective for closed-loop, time-varying systems.
  • The approach provides accurate system matrix estimation, overcoming common identification problems.
  • This method has practical implications for fault detection and control engineering.