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

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)1944-1951
Number of pages8
JournalIEEE Robotics and Automation Letters
Issue number2
Publication statusPublished - 2022 Apr 1

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Biomedical Engineering
  • Human-Computer Interaction
  • Mechanical Engineering
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Control and Optimization
  • Artificial Intelligence


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