Topic: Data-Enhanced Model-Based Safe Cooperative Control

With the rapid advancement of artificial intelligence, the Internet of Things, and edge computing technologies, Multi-Agent Systems (MAS) have been extensively deployed in intelligent transportation, smart grids, unmanned aerial vehicle formations, and smart manufacturing systems. By leveraging distributed cooperative control mechanisms, MAS enable multiple agents to accomplish complex global tasks through local interactions. Among the fundamental challenges in this field, achieving efficient and robust consensus in dynamic and uncertain environments remains a key research challenge.

In practical scenarios, multi-agent systems are inevitably exposed to various uncertainties and network-induced imperfections. Model uncertainties, unknown nonlinear dynamics, external disturbances, communication delays, packet losses, and even malicious cyber-attacks significantly complicate the design of distributed cooperative control protocols. These factors can degrade convergence performance, violate safety constraints, or even destabilize the entire networked system. As MAS are increasingly deployed in safety-critical and mission-critical applications, guaranteeing robustness, resilience, and safety under such adverse conditions is more important than ever.

Consequently, the development of advanced control methodologies capable of addressing uncertainties, communication constraints, and security threats is of substantial theoretical and practical importance. In particular, integrating rigorous model-based control frameworks with emerging data-driven and learning-enhanced techniques offers a promising pathway toward achieving safe, adaptive, and resilient cooperative behaviors in complex networked environments.

Multi-agent systems, cooperative control, safe control, model-based control, data-driven methods.