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

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.