A Robust Safety–Critical Control Framework for Control Affine Systems With Applications to AUVs
Y. Jiang, Chenggang Wang, Bochen Li, Lei Song, Xinping Guan
Abstract
This article proposes a robust safety–critical control framework based on high order control barrier function (HOCBF) and disturbance observer (DOB) for control affine systems in the presence of unknown disturbances. Specifically, we apply the proposed framework for the obstacle avoidance of autonomous underwater vehicles (AUVs) to consider the nonlinear characteristics and current disturbance. The system stability is proved to be uniformly ultimately bounded (UUB). The proposed framework first estimates the unknown term of the Lie derivative of HOCBF by employing a first-order high-gain DOB. The bounded property of the DOB estimation error is combined with the Tunable Input-to-State Safe (TISSf) to compensate for the forward invariant condition of CBF. We construct the DOB-TISSf-based robust safety–critical control framework based on quadratic programming to guarantee safety and control performance simultaneously. We show that the safety–critical framework still renders the system UUB. Simulations and field experiments are validated on AUV to avoid obstacles. The results demonstrate that the proposed framework ensures the safety of AUV. Moreover, the framework allows for lower conservativeness of the disturbed system and thus a better tradeoff between system performance and safety.