Event-Triggered Interval Observer Fault Detection and Isolation for Multiagent Systems.

Area of Science:

• Control Systems Engineering
• Distributed Systems
• Fault Detection and Diagnosis

Background:

• Multiagent systems require robust fault detection and isolation (FDI) for reliable operation.
• Traditional FDI methods often suffer from high communication overhead.
• Event-triggered mechanisms offer a promising solution to reduce communication load.

Purpose of the Study:

• To develop an event-triggered interval observer (ETIO) for efficient FDI in multiagent systems.
• To optimize the ETIO for a balance between disturbance robustness and fault sensitivity.
• To enable distributed fault isolation using local and neighbor information.

Main Methods:

• Design of an event-triggered mechanism to minimize communication.
• Integration of the event-triggered mechanism with an interval observer to create a distributed ETIO.
• Formulation of the ETIO as a multiobjective optimization problem (l1/H∞ performance).
• Implementation of a bank of ETIOs for local fault isolation.

Main Results:

• The proposed ETIO significantly reduces communication transmissions compared to traditional methods.
• The multiobjective optimization achieves a favorable trade-off between robustness and sensitivity.
• The bank of ETIOs successfully isolates faulty agents using local and neighbor data.
• Comparative analysis demonstrates the superiority of the ETIO method.

Conclusions:

• The developed ETIO method provides an efficient and effective solution for fault detection and isolation in multiagent systems.
• The event-triggered approach enhances communication efficiency while maintaining high performance.
• The methodology is validated through simulations on a multi-unmanned aerial vehicles (UAVs) system.