Fuzzy adaptive event-triggered distributed control for a class of nonlinear multi-agent systems

Area of Science:

• Control Systems Engineering
• Artificial Intelligence
• Robotics

Background:

• Multi-agent systems (MASs) present complex control challenges, particularly nonlinear systems.
• Event-triggered control strategies aim to reduce communication and computational load in distributed systems.
• Adaptive control is crucial for systems with uncertainties and changing dynamics.

Purpose of the Study:

• To develop an adaptive and event-triggered distributed controller for nonlinear MASs.
• To ensure robust tracking performance and stability in leader-follower configurations.
• To enhance control efficiency by minimizing data transmission through event-triggering.

Main Methods:

• Utilizing a fuzzy adaptive control strategy.
• Employing a Lyapunov-based filter for stability analysis.
• Applying the backstepping recursion technique for controller design.
• Implementing an event-triggering mechanism to optimize control updates.

Main Results:

• Guaranteed convergence of tracking errors between leader and follower agents to a bounded region near the origin.
• Demonstrated semi-global, uniform, and final boundedness of all closed-loop signals via Lyapunov stability theory.
• Validation of the control mechanism's effectiveness through simulation tests.

Conclusions:

• The proposed fuzzy adaptive event-triggered distributed controller effectively manages nonlinear MASs.
• The control strategy ensures system stability and accurate tracking performance.
• Event-triggered mechanisms offer a promising approach for efficient distributed control in MASs.