Safety-Critical Control With Nonaffine Control Inputs Via a Relaxed Control Barrier Function for an Autonomous Vehicle
Joohwan Seo, Joonho Lee, Eunkyu Baek, Roberto Horowitz, Jongeun Choi
Abstract
When designing a controller for the autonomous vehicle system, safety and trajectory tracking performance are two major concerns. This letter proposes a novel control design for an autonomous vehicle system with nonaffine control inputs that can track the desired trajectories while considering the safety constraint. First, the vehicle dynamics is modeled using the differential flatness approach. The dynamic inversion method is then employed for the trajectory tracking of a nonaffine-in control system, and a control barrier function (CBF) approach is utilized to enforce the safety constraint. The trajectory tracking control is handled as a least-squares optimization problem, while the CBF is considered as a constraint. By relaxing the CBF constraint to the cost function, the novel control design is derived via a dynamic inversion. The safety and the stability of the closed-loop system are analyzed using a singular perturbation method. The proposed method is validated using numerical simulation and the high-fidelity car simulator under realistic driving scenarios.