Event-triggered consensus of multi-agent systems with uncertain control gain via distributed fuzzy logic observer

Area of Science:

• Control Theory
• Robotics
• Artificial Intelligence

Background:

• Multi-agent systems (MAS) face challenges in achieving consensus due to uncertainties and partial observability.
• Existing control methods often require full state information or constant communication, limiting practical application.

Purpose of the Study:

• To develop an event-triggered adaptive backstepping control for leader-following consensus in uncertain nonlinear high-order MAS.
• To address partial observability and unknown control gains in these systems.

Main Methods:

• Adaptive distributed observers for leader state estimation and local state observers for follower state reconstruction.
• Fuzzy logic systems for modeling unknown nonlinear dynamics.
• A novel relative threshold event-triggered scheme to minimize data exchange.

Main Results:

• The proposed controller effectively achieves leader-following consensus in complex, partially observable scenarios.
• The event-triggered scheme significantly reduces communication frequency while maintaining near-zero tracking errors.
• Simulation results validate the controller's effectiveness and superiority over existing methods.

Conclusions:

• The developed control methodology offers a robust and efficient solution for consensus problems in uncertain nonlinear MAS.
• The integration of adaptive observers, fuzzy logic, and event-triggered mechanisms enhances system performance and reduces communication load.