2024/10/15 by Luzia Knoedler, Oswin So, Knoedler, Luzia +13 · 5 citations
Computer Science · Engineering · #FOS: Computer and information sciences #FOS: Mathematics #Fault Detection and Control Systems #Optimization and Control (math.OC) #Robotics (cs.RO) #Smart Grid Security and Resilience #Software Reliability and Analysis Research
paper · pdf · doi:10.48550/arxiv.2410.11157
openalex publication_date 2024/10/15 · openalex created_date 2024/10/20 · openalex updated_date 2026/07/28
Control Barrier Functions (CBFs) have proven to be an effective tool for performing safe control synthesis for nonlinear systems. However, guaranteeing safety in the presence of disturbances and input constraints for high relative degree systems is a difficult problem. In this work, we propose the Robust Policy CBF (RPCBF), a practical approach for constructing robust CBF approximations online via the estimation of a value function. We establish conditions under which the approximation qualifies as a valid CBF and demonstrate the effectiveness of the RPCBF-safety filter in simulation on a variety of high relative degree input-constrained systems. Finally, we demonstrate the benefits of our method in compensating for model errors on a hardware quadcopter platform by treating the model errors as disturbances. Website including code: www.oswinso.xyz/rpcbf/