Deriving overtaking strategy from nonlinear model predictive control for a race car

Alexander Buyval; Aidar Gabdulin; Ruslan Mustafin; Ilya Shimchik

IROS 2017poster37 citations

Deriving overtaking strategy from nonlinear model predictive control for a race car

Alexander Buyval, Aidar Gabdulin, Ruslan Mustafin, Ilya Shimchik

Abstract

Car racing control is often assumed to take place in isolation, although the greatest strategic challenges arise in the presence of other cars. Our solution uses a combined nonlinear system model which includes the four-wheeled vehicle dynamics model and point-mass model of its opponent car. This proposed combined model allows the optimal control scheme to automatically find an overtaking strategy. The FORCES code generation tool to handle the consequent computational complexity of the Nonlinear Model Predictive Controller (NMPC). We demonstrate the practicality of the suggested method by performing real-time control in a racing simulation software.

BibTeX

@inproceedings{iros2017_derivingovertaki,
  title = {Deriving overtaking strategy from nonlinear model predictive control for a race car},
  author = {Alexander Buyval and Aidar Gabdulin and Ruslan Mustafin and Ilya Shimchik},
  booktitle = {IROS 2017},
  year = {2017}
}