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Simultaneous Sensor and Actuator Fault Reconstruction by Using a Sliding Mode Observer, Fuzzy Stability Analysis, and

Samira Asadi1, Mehrdad Moallem1, G Gary Wang1

  • 1School of Mechatronic Systems Engineering, Simon Fraser University, Surrey, BC V3T 0A3, Canada.

Sensors (Basel, Switzerland)
|September 23, 2022
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Summary
This summary is machine-generated.

This study introduces a novel Takagi-Sugeno (TS) fuzzy sliding mode observer (SMO) for reconstructing simultaneous actuator and sensor faults in nonlinear systems. The method offers improved fault diagnosis by using nonlinear optimization and a non-quadratic Lyapunov function.

Keywords:
H∞ performanceTS fuzzy systemactuator and sensor faultsfault reconstructionfminconnon-quadratic Lyapunov function (NQLF)sliding mode observer (SMO)

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Area of Science:

  • Control Systems Engineering
  • Nonlinear System Analysis
  • Fault Diagnosis

Background:

  • Faults in actuators and sensors can lead to system instability and performance degradation.
  • Existing fault reconstruction methods often struggle with nonlinear systems and immeasurable variables.
  • Robust fault diagnosis is crucial for safety and reliability in complex dynamic systems.

Purpose of the Study:

  • To develop a Takagi-Sugeno (TS) fuzzy sliding mode observer (SMO) for simultaneous actuator and sensor fault reconstruction.
  • To address nonlinear systems with immeasurable premise variables and unknown disturbances.
  • To enhance fault diagnosis accuracy and robustness in dynamic systems.

Main Methods:

  • Representing the nonlinear system using a TS fuzzy model with immeasurable premise variables.
  • Constructing an augmented system to combine actuator and sensor faults.
  • Employing H∞ performance criteria for disturbance minimization.
  • Designing SMO gains using nonlinear optimization (fmincon) and a non-quadratic Lyapunov function (NQLF).

Main Results:

  • Successfully reconstructed simultaneous actuator and sensor faults in a nonlinear system.
  • Demonstrated robustness against unknown disturbances using H∞ criteria.
  • Validated the proposed approach on a continuous stirred tank reactor (CSTR) model.
  • Achieved superior performance compared to existing methods through numerical simulations.

Conclusions:

  • The proposed TS fuzzy SMO effectively reconstructs simultaneous faults in nonlinear systems.
  • The use of nonlinear optimization and NQLF offers advantages over traditional LMI-based methods.
  • The approach is robust to uncertainties and suitable for systems with immeasurable premise variables.