Performance-Based Fault Detection and Fault-Tolerant Control for Nonlinear Systems With T-S Fuzzy Implementation

Area of Science:

• Control Engineering
• Nonlinear System Analysis
• Fault Diagnosis and Control

Background:

• Nonlinear systems present challenges in maintaining performance under fault conditions.
• Existing fault detection and control methods may not adequately address performance degradation.

Purpose of the Study:

• To develop a performance-based fault detection (FD) and fault-tolerant control (FTC) scheme for nonlinear systems.
• To introduce a fault-tolerant margin for quantifying system resilience.
• To recover system performance after fault occurrence.

Main Methods:

• Utilized nonlinear factorization techniques for controller parameterization.
• Developed an FD scheme to estimate and detect stability performance degradation.
• Presented a performance-based FTC strategy for performance recovery.
• Applied Takagi-Sugeno fuzzy dynamic modeling for scheme design.

Main Results:

• Successfully designed and investigated a performance-based FD and FTC scheme.
• Introduced the fault-tolerant margin as a key performance indicator.
• Demonstrated the effectiveness of the proposed FTC strategy in recovering system performance.
• Validated the approach through a case study on a three-tank system.

Conclusions:

• The proposed performance-based FD and FTC scheme effectively addresses fault impacts on nonlinear systems.
• The Takagi-Sugeno fuzzy modeling approach provides a viable design methodology.
• The fault-tolerant margin serves as a useful metric for system fault tolerance.