Where to Put Safety? Control Barrier Function Placement in Networked Control Systems
This addresses safety placement for autonomous cyber-physical systems, offering an incremental improvement by integrating predictive and local mechanisms.
The paper tackled the problem of where to enforce safety in networked control systems with delayed communication, comparing local and remote control barrier function (CBF) strategies and showing that local CBFs provide higher disturbance tolerance while remote MPC-CBF enables better performance but stricter disturbance limits. They proposed a combined architecture and demonstrated results in simulations on a robot collision-avoidance task.
Ensuring safe behavior is critical for modern autonomous cyber-physical systems. Control barrier functions (CBFs) are widely used to enforce safety in autonomous systems, yet their placement within networked control architectures remains largely unexplored. In this work, we investigate where to enforce safety in a networked control system in which a remote model predictive controller (MPC) communicates with the plant over a delayed network. We compare two safety strategies: i) a local myopic CBF filter applied at the plant and ii) predictive CBF constraints embedded in the remote MPC. For both architectures, we derive state-dependent disturbance tolerance bounds and show that safety placement induces a fundamental trade-off: local CBFs provide higher disturbance tolerance due to access to fresh state measurements, whereas MPC-CBF enables improved performance through anticipatory behavior, but yields stricter admissible disturbance levels. Motivated by this insight, we propose a combined architecture that integrates predictive and local safety mechanisms. The theoretical findings are illustrated in simulations on a planar three-degree-of-freedom robot performing a collision-avoidance task.