Minimum rational entropy fault-tolerant control for nonlinear stochastic distribution control systems with quantized...

Area of Science:

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

Background:

• Quantized nonlinear stochastic distribution control (SDC) systems face challenges with actuator and sensor faults.
• Existing fault diagnosis (FD) and fault-tolerant control (FTC) methods may struggle with quantization effects and simultaneous fault estimation.

Purpose of the Study:

• To develop a robust fault-tolerant control (FTC) strategy for quantized nonlinear stochastic distribution control (SDC) systems.
• To address simultaneous estimation and compensation of both actuator and sensor faults.
• To ensure system stability and performance despite fault occurrences and quantization.

Main Methods:

• A two-step fuzzy modeling approach was used to create static and dynamic system models.
• An adaptive augmented observer was designed for simultaneous fault estimation, accounting for quantization.
• A comprehensive FTC strategy integrated a virtual sensor compensator with a minimum rational entropy (MRE) controller.

Main Results:

• The adaptive augmented observer successfully estimated actuator and sensor faults under quantization.
• The proposed FTC strategy effectively compensated for faults and maintained system stability.
• The methodology demonstrated practical effectiveness in a molecular weight distribution system.

Conclusions:

• The developed FTC strategy offers a robust solution for quantized nonlinear SDC systems with simultaneous faults.
• The integration of fuzzy modeling, adaptive observers, and MRE control provides a powerful framework for fault compensation.
• The validated application highlights the potential of this approach in real-world engineering systems.