Fuzzy extended state observer for the fault detection and identification

Area of Science:

• Control Systems Engineering
• Nonlinear System Analysis
• Fuzzy Logic Applications

Background:

• Autonomous nonlinear systems are susceptible to faults that can compromise their performance and safety.
• Existing fault detection methods may lack robustness or require complex models.
• Accurate fault estimation is crucial for effective fault-tolerant control.

Purpose of the Study:

• To develop and validate a novel fault detection and identification methodology for autonomous nonlinear systems.
• To design a fuzzy extended system observer (FESO) capable of estimating system faults.
• To ensure the stability and boundedness of the proposed observer under Lyapunov criteria.

Main Methods:

• Implementation of a Mamdani-type fuzzy system within an extended system observer framework.
• Utilizing observer error as the fuzzy input variable for a single-input single-output (SISO) fuzzy system.
• Employing Lyapunov stability analysis to verify the ultimate boundedness of the FESO solutions.

Main Results:

• The proposed fuzzy extended system observer (FESO) effectively estimates faults in autonomous nonlinear systems.
• Stability analysis confirms that the FESO solutions are ultimately bounded, ensuring reliable operation.
• Simulation examples demonstrate the practical feasibility and accuracy of the FESO methodology.

Conclusions:

• The developed FESO provides a robust and effective approach for fault detection and identification in nonlinear systems.
• The fuzzy logic-based observer offers advantages in handling system uncertainties and nonlinearities.
• The methodology is validated through simulations, confirming its potential for real-world applications.