A Tricycle Model to Accurately Control an Autonomous Racecar with Locked Differential
Abstract
In this paper, we present a novel formulation to model the effects of a locked differential on the lateral dynamics of an autonomous open-wheel racecar. The model is used in a Model Predictive Controller in which we included a micro-steps discretization approach to accurately linearize the dynamics and produce a prediction suitable for real-time implementation. The stability analysis of the model is presented, as well as a brief description of the overall planning and control scheme which includes an offline trajectory generation pipeline, an online local speed profile planner, and a low-level longitudinal controller. An improvement of the lateral path tracking is demonstrated in preliminary experimental results that have been produced on a Dallara AV-21 during the first Indy Autonomous Challenge event on the Monza F1 racetrack. Final adjustments and tuning have been performed in a high-fidelity simulator demonstrating the effectiveness of the solution when performing close to the tire limits.
Keywords
Cite
@article{arxiv.2312.14808,
title = {A Tricycle Model to Accurately Control an Autonomous Racecar with Locked Differential},
author = {Ayoub Raji and Nicola Musiu and Alessandro Toschi and Francesco Prignoli and Eugenio Mascaro and Pietro Musso and Francesco Amerotti and Alexander Liniger and Silvio Sorrentino and Marko Bertogna},
journal= {arXiv preprint arXiv:2312.14808},
year = {2024}
}
Comments
Presented at the 2023 IEEE 11th International Conference on Systems and Control, Sousse, Tunisia, December 18-20, 2023