文章基本信息

标题：NMPC for Racing Using a Singularity-Free Path-Parametric Model with Obstacle Avoidance
本地全文：下载
作者：Daniel Kloeser ; Tobias Schoels ; Tommaso Sartor 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2020
卷号：53
期号：2
页码：14324-14329
DOI：10.1016/j.ifacol.2020.12.1376
语种：English
出版社：Elsevier
摘要：AbstractThis work presents the real-time control of 1:43 scale autonomous race cars using nonlinear model predictive control based on a singularity-free prediction model. This model allows the car to drive at both low and high speeds and in stop-and-go maneuvers. Additional constraints are imposed in the optimal control problem to ensure the validity of the model assumptions. Moreover, the control scheme is capable of avoiding obstacles online. The experimental results show that the proposed method converges to nearly time-optimal behavior by maximizing the progress on the track and achieves competitive lap time results.
关键词：KeywordsNonlinearoptimal automotive controlobstacle avoidanceautomotive system identificationmodellingautonomous vehicles