Neural-Network-Based Distributed Asynchronous Event-Triggered Consensus Tracking of a Class of Uncertain Nonlinear...

Area of Science:

• Control Systems Engineering
• Artificial Intelligence
• Networked Systems

Background:

• Distributed consensus tracking is crucial for multi-agent systems.
• Existing methods often require continuous communication and multiple neural networks, increasing resource demands.
• Uncertain nonlinearities and directed network topologies present significant challenges.

Purpose of the Study:

• To develop an adaptive asynchronous event-triggered control strategy for uncertain lower triangular nonlinear multi-agent systems.
• To reduce communication and computational load by utilizing intermittent, event-driven data exchange.
• To enhance the robustness and efficiency of distributed consensus tracking.

Main Methods:

• A single-neural network-based adaptive asynchronous event-triggered design is proposed.
• A distributed event-triggered estimator is developed to estimate the leader signal using neighbors' triggered outputs.
• Local trackers are designed using the estimated leader signal and a triggering law.
• Lyapunov stability theorem is employed to analyze the closed-loop system stability.

Main Results:

• The proposed method achieves distributed consensus tracking using a single neural network per follower.
• Asynchronous and intermittent communication is effectively managed under a directed network.
• Significant savings in communicational and computational resources are demonstrated.
• Comparative simulations confirm the effectiveness of the proposed control strategy.

Conclusions:

• The developed asynchronous event-triggered consensus tracking methodology is effective for uncertain nonlinear multi-agent systems.
• The single-neural network approach significantly reduces system resource requirements.
• The strategy ensures stability and efficient tracking performance in directed network environments.