Where to Put Safety? Control Barrier Function Placement in Networked Control Systems

TL;DR

This paper explores optimal placement of control barrier functions in networked control systems, comparing local and predictive strategies, and proposes a combined approach for enhanced safety and performance.

Contribution

It introduces a theoretical framework for safety placement in networked control systems, analyzing trade-offs between local and predictive CBF strategies, and proposes a combined architecture.

Findings

Local CBFs offer higher disturbance tolerance due to fresh measurements.

Predictive CBFs enable anticipatory safety behavior but are more sensitive to disturbances.

A combined architecture leverages both strategies for improved safety and performance.

Abstract

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.