Cooperative Control of Multiagent Systems: A Quantization Feedback-Based Event-Triggered Approach

Area of Science:

• Control Systems Engineering
• Artificial Intelligence

Background:

• Synchronization tracking in high-order uncertain nonlinear multiagent systems presents significant challenges.
• Existing methods often require continuous communication and computation, leading to inefficiency.

Purpose of the Study:

• To develop an event-triggered neuroadaptive control method for synchronization tracking in high-order uncertain nonlinear multiagent systems.
• To reduce communication and computation load through intermittent feedback and parameter updates.

Main Methods:

• A novel storer-based triggering transmission strategy was employed for state channels.
• An event-triggered neuroadaptive control method with quantitative state feedback was proposed.
• A dual-phase technique was used for intermittent updating of neural network weights.

Main Results:

• The proposed method avoids continuous control updates by performing parameter estimations at trigger instants.
• Lower-frequency triggering transmissions were achieved using a single event detector per agent.
• Tracking and disagreement errors were steered into an adjustable neighborhood near the origin.
• A strictly positive dwell time was proven to prevent Zeno behavior.

Conclusions:

• The developed event-triggered neuroadaptive control scheme is efficient for synchronization tracking in complex multiagent systems.
• The strategy conserves communication and computational resources effectively.
• Theoretical analysis and simulations confirm the protocol's validity and performance.