Safety-Critical Control for Non-affine Nonlinear Systems with Application on Autonomous Vehicle

Abstract

This paper presents a novel control framework to handle safety-critical control for non-affine nonlinear systems. The proposed control development is considered to deal with safety-critical aspects in autonomous vehicle driving. The safety constraints are guaranteed using control barrier function (CBF), which implies forward-invariance of a safe set. In particular, we focus on CBF that enforces strict state-dependent high relative degree constraints for general nonlinear vehicle models. Moreover, the CBF safety constraints are incorporated into a nonlinear model predictive control (NMPC) framework. The advantage is twofold. First, both vehicle driving safety and comfort performance can be improved. Second, it helps to reduce computational burden in real time NMPC implementation. The proposed algorithm is validated and compared with conventional NMPC in several safety-critical scenarios including sudden objects and road boundaries avoidance, showing improvements in both safety and smooth driving. The validation is conducted on high fidelity vehicle dynamics and traffic environment simulation models.