Neural Network-Based Distributed Cooperative Learning Control for Multiagent Systems via Event-Triggered...

Area of Science:

• Control Engineering
• Artificial Intelligence
• Distributed Systems

Background:

• Adaptive neural control systems often require complex communication for cooperative learning.
• Existing methods may suffer from continuous communication or lack guarantees for generalization and Zeno behavior avoidance.

Purpose of the Study:

• To propose an event-based distributed cooperative learning (DCL) law for adaptive neural control systems with identical plant structures but different reference signals.
• To ensure convergence of neural network weights and guarantee generalization ability under event-triggered conditions.

Main Methods:

• An event-based DCL law is developed where agents broadcast neural network weight estimations based on their own weights.
• Communication topology is assumed to be connected and undirected.
• Analysis guarantees convergence to optimal NN weights and avoids Zeno behavior by ensuring positive inter-event intervals.

Main Results:

• The proposed DCL law ensures that neural network weights converge to a small neighborhood of their optimal values.
• Generalization ability of neural networks is maintained, with the approximation domain being the union of all system trajectories.
• A strictly positive lower bound on inter-event intervals is guaranteed, preventing Zeno behavior.

Conclusions:

• The event-based DCL law is effective for adaptive neural control systems, enabling efficient cooperative learning.
• The method ensures robust performance, including guaranteed generalization and avoidance of Zeno phenomena.
• Numerical simulations validate the proposed learning law's effectiveness.