A virtual-structure-based type-3 fuzzy system for predictive sensor and actuator fault detection, compensation, and...

Area of Science:

• Control Engineering
• Artificial Intelligence
• Nonlinear System Analysis

Background:

• Complex nonlinear systems are susceptible to actuator and sensor faults.
• System dynamics are often unknown, necessitating black-box modeling approaches.
• Existing fault-tolerant control strategies may not adequately address unknown system dynamics.

Purpose of the Study:

• To develop an active fault-tolerant control (FTC) strategy for nonlinear systems with unknown dynamics.
• To address simultaneous actuator and sensor faults.
• To utilize a black-box modeling approach for system identification.

Main Methods:

• A three-subsystem framework integrating Type-3 fuzzy logic systems, model predictive control (MPC), and adaptive stabilization.
• Sensor fault diagnosis and compensation using Type-3 fuzzy estimators and a supervisory module.
• Implementation of a virtual structure including virtual components for enhanced fault management.

Main Results:

• Successful modeling of unknown nonlinear dynamics using Type-3 fuzzy logic.
• Effective diagnosis and compensation of sensor faults.
• Demonstration of an active FTC strategy capable of handling both actuator and sensor faults in black-box nonlinear systems.

Conclusions:

• The proposed active fault-tolerant control strategy is effective for nonlinear systems with unknown dynamics.
• The integration of Type-3 fuzzy logic and MPC provides robust fault handling.
• The black-box approach simplifies system identification for fault-tolerant control design.