Neural-Network-Based Event-Triggered Adaptive Control of Nonaffine Nonlinear Multiagent Systems With Dynamic...

Area of Science:

• Control Systems Engineering
• Artificial Intelligence
• Nonlinear Dynamics

Background:

• Nonlinear multiagent systems present challenges due to nonaffine structures, pure-feedback configurations, and disturbances.
• Unmodeled dynamics and input dead-zones complicate control design in such systems.
• Existing control strategies often incur significant communication burdens.

Purpose of the Study:

• To develop an adaptive event-triggered neural control strategy for nonaffine pure-feedback nonlinear multiagent systems.
• To address dynamic disturbances, unmodeled dynamics, and dead-zone inputs.
• To reduce communication load through a novel event-triggered mechanism.

Main Methods:

• Utilizing radial basis function neural networks for approximating unknown nonlinear functions.
• Introducing a dynamic signal to manage unmodeled dynamics.
• Designing an event-triggered control protocol with a varying threshold.
• Applying Lyapunov function methods for stability analysis.

Main Results:

• Achieved convergence of follower outputs to a neighborhood of the leader's output.
• Ensured boundedness of all signals within the closed-loop system.
• Demonstrated the effectiveness of the proposed adaptive neural control and event-triggered strategy through simulations.

Conclusions:

• The proposed adaptive event-triggered neural control effectively manages complex nonlinear multiagent systems.
• The event-triggered strategy significantly reduces communication requirements.
• The control scheme guarantees system stability and performance in the presence of uncertainties and disturbances.