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A Data-Driven Predictive Control Driver for Racing Car Simulation

Fulltext:


Authors:

Ruslan Shaiakhmetov , Danilo Pianini , Valter Venusti , Alessandro Papadopoulos

Research groups:

Robotics
Complex Real-Time Embedded Systems

Publication Type:

Conference/Workshop Paper

Venue:

8th International Symposium on Dis- tributed Simulation and Real Time Applications

Abstract

The capability to accurately simulate the behavior of a racing car is paramount in modern-day racing competitions to quickly find a good base setup to kick-start the work on track. Typically, a professional driver is employed to drive the simulated race car and provide feedback. However, this operation is expensive and time-consuming, as capable human drivers quickly become a bottleneck. In conjunction with highly accurate simulations of the physical car’s behavior, a capable virtual driver could thus accelerate the car setup and development to a great extent. In this paper, we propose to apply a data-driven predictive control approach called Data-enabled Predictive Control to model a racing driver by tracking a pre-defined trajectory. We compare our proposed approach with an industrial first-choice Proportional-Integral-Derivative controller and state-of-the-art Model Predictive Control controller, finding that the approach is feasible and it can provide significant improvements over the state-of-the-art, especially for trajectories whose feasibility is at the edge of the car’s capabilities.

Bibtex

@inproceedings{Shaiakhmetov7026,
author = {Ruslan Shaiakhmetov and Danilo Pianini and Valter Venusti and Alessandro Papadopoulos},
title = {A Data-Driven Predictive Control Driver for Racing Car Simulation},
month = {October},
year = {2024},
booktitle = {8th International Symposium on Dis- tributed Simulation and Real Time Applications},
url = {http://www.es.mdu.se/publications/7026-}
}