Distributed Predictive Control Barrier Functions: Towards Scalable Safety Certification in Modular Multi-Agent Systems

2026-03-31

AI Summary

This work addresses the lack of scalable, formally guaranteed safety in distributed multi-agent systems operating under dynamic network topologies and external disturbances. To this end, the authors propose a safety-critical control framework that integrates structured Control Barrier Functions (s-CBFs) with Distributed Predictive Control Barrier Functions (D-PCBFs). By leveraging a model-predictive optimization layer, the framework ensures recoverable safety while supporting plug-and-play capabilities for agents. The key innovation lies in the design of the first plug-and-play protocol that enables safety recovery under dynamically changing communication topologies, combined with real-time optimization and formal verification mechanisms. Experimental validation through simulations and physical micro-racing car platooning demonstrates that the approach effectively maintains system safety and scalability during agent join/leave events.

Abstract

We consider safety-critical multi-agent systems with distributed control architectures and potentially varying network topologies. While learning-based distributed control enables scalability and high performance, a lack of formal safety guarantees in the face of unforeseen disturbances and unsafe network topology changes may lead to system failure. To address this challenge, we introduce structured control barrier functions (s-CBFs) as a multi-agent safety framework. The s-CBFs are augmented to a distributed predictive control barrier function (D-PCBF), a predictive, optimization-based safety layer that uses model predictions to guarantee recoverable safety at all times. The proposed approach enables a permissive yet formal plug-and-play protocol, allowing agents to join or leave the network while ensuring safety recovery if a change in network topology requires temporarily unsafe behavior. We validate the formulation through simulations and real-time experiments of a miniature race-car platoon.

Problem

• multi-agent systems
• safety certification
• distributed control
• network topology changes
• scalability

Innovation

• structured control barrier functions
• distributed predictive control
• safety certification
• multi-agent systems
• plug-and-play protocol