Neural network-based fault detection for nonlinear networked systems with uncertain medium access constraint:...

Area of Science:

• Control Systems Engineering
• Nonlinear Systems Analysis
• Fault Detection and Diagnosis

Background:

• Networked control systems (NCS) face challenges with medium access constraints and sensor uncertainties.
• Ensuring reliable operation of NCS requires robust fault detection mechanisms.
• Partially unknown transition probabilities between sensors add complexity to fault detection.

Purpose of the Study:

• To develop a fault detection method for continuous-time nonlinear NCS with medium access constraints.
• To address uncertainties in sensor transition probabilities within the fault detection framework.
• To ensure the stochastic stability and effectiveness of the proposed fault detection system.

Main Methods:

• A Markovian system approach models the sensor access process.
• A robust filter-based residual generator is designed for fault indication.
• Neural networks approximate nonlinear terms, and Lyapunov-Krasovskii functionals analyze system stability.
• Sufficient conditions for stochastic stability are derived using matrix inequalities.

Main Results:

• The proposed method effectively detects faults in nonlinear NCS.
• The fault detection system demonstrates stochastic stability under uncertain conditions.
• Filter gains are calculated by solving matrix inequalities, ensuring robust performance.
• Simulations using a DC motor system validate the fault detector's effectiveness.

Conclusions:

• The developed fault detection technique is effective for nonlinear NCS with medium access constraints.
• The approach successfully handles partially unknown sensor transition probabilities.
• The study provides a robust framework for enhancing the reliability of networked control systems.