An Online Evolving Method For a <i>Safe</i> and <i>Fast</i> Automated Vehicle Control System

Han, Teawon; Nageshrao, Subramanya; Filev, Dimitar; Redmill, Keith; Özgüner, Ü.

doi:10.1109/tsmc.2021.3128193

Cited by 4 publications

(1 citation statement)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To achieve safety in the control process, it is needed to describe the safety in a way that is easy to solve [9]. The control barrier function (CBF) method converting safety target into CBF-based linear constraints is a promising way to ensure safety during the control process [10].…”

mentioning

confidence: 99%

Suboptimal Safety-Critical Control for Continuous Systems Using Prediction-Correction Online Optimization

Wang¹,

wen²,

Shi³

et al. 2022

Preprint

View full text Add to dashboard Cite

This paper investigates the control barrier function (CBF) based safety-critical control for continuous nonlinear control affine systems using more efficient online algorithms by the time-varying optimization method. The idea of the algorithms is that when quadratic programming (QP) or other convex optimization algorithms needed in the CBF-based method is not computation affordable, the alternative suboptimal feasible solutions can be obtained more economically. By using the barrier-based interior point method, the constrained CBF-QP problems are transformed into unconstrained ones with suboptimal solutions tracked by two continuous descent-based algorithms. Considering the lag effect of tracking and exploiting the system information, the prediction method is added to the algorithms, which achieves exponential convergence to the time-varying suboptimal solutions. The convergence and robustness of the designed methods as well as the safety criteria of the algorithms are studied theoretically. The effectiveness is illustrated by simulations on the antiswing and obstacle avoidance tasks.

show abstract

mentioning

confidence: 99%