Distributed simultaneous fault detection and leader-following consensus control for multi-agent systems

Area of Science:

• Control Systems Engineering
• Networked Systems
• Robotics

Background:

• Distributed multi-agent systems require robust control and fault detection.
• Existing methods often use separate fault detection and control modules, increasing complexity.
• Leader-following consensus control is crucial for coordinated agent behavior.

Purpose of the Study:

• To develop a single module for simultaneous fault detection and leader-following consensus control (SFDLCC) in multi-agent networks.
• To enhance fault isolation and fault type identification (sensor vs. actuator).
• To attenuate the effects of unknown inputs on tracking error and residual signals.

Main Methods:

• Design of an SFDLCC module for each agent using extended linear matrix inequality (LMI) techniques.
• Integration of neighboring agent data and local relative measurements.
• Application of finite frequency H∞ and H- performance indices for disturbance and fault attenuation/enhancement.

Main Results:

• A unified module successfully performs both fault detection and consensus control.
• The proposed method effectively attenuates disturbances and noise effects.
• Faulty agents are isolated, and the fault type (sensor or actuator) is identified.
• Finite frequency performance indices ensure desired H∞ and H- properties.

Conclusions:

• The integrated SFDLCC approach offers a more efficient solution compared to separate modules.
• The methodology provides robust performance in the presence of faults, disturbances, and noise.
• Simulation results validate the effectiveness of the proposed approach for networked multi-agent systems.