Robust Control Barrier Functions for Safe Control Under Uncertainty Using Extended State Observer and Output Measurement

Abstract

Control barrier functions-based quadratic programming (CBF-QP) is gaining popularity as an effective controller synthesis tool for safe control. However, the provable safety is established on an accurate dynamic model and access to all states. To address such a limitation, this paper proposes a novel design combining an extended state observer (ESO) with a CBF for safe control of a system with model uncertainty and external disturbances only using output measurement. Our approach provides a less conservative estimation error bound than other disturbance observer-based CBFs. Moreover, only output measurements are needed to estimate the disturbances instead of access to the full state. The bounds of state estimation error and disturbance estimation error are obtained in a unified manner and then used for robust safe control under uncertainty. We validate our approach's efficacy in simulations of an adaptive cruise control system and a Segway self-balancing scooter.