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, we propose a novel design combining an extended state observer (ESO) or machine learning with a CBF for safe control of a system with model uncertainty and external disturbances.

Related Publications

1. Robust Control Barrier Functions for Safe Control Under Uncertainty Using Extended State Observer and Output Measurement, Jinfeng Chen*, Zhiqiang Gao, and Qin Lin, IEEE Conference on Decision and Control (CDC), 2023 [Paper]
2. Online Adaptive Compensation for Model Uncertainty Using Extreme Learning Machine-based Control Barrier Functions, Emanuel Munoz Panduro*, Dvij Kalaria, Qin Lin, and John M. Dolan, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2022 [Paper][Video]