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arXiv · 2312.14808

A Tricycle Model to Accurately Control an Autonomous Racecar with Locked Differential

Ayoub Raji·Nicola Musiu·Alessandro Toschi·Francesco Prignoli·Eugenio Mascaro·Pietro Musso·Francesco Amerotti·Alexander Liniger·Silvio Sorrentino·Marko Bertogna
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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.

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Ayoub Raji, Nicola Musiu, Alessandro Toschi, Francesco Prignoli, Eugenio Mascaro, Pietro Musso, Francesco Amerotti, Alexander Liniger, Silvio Sorrentino, Marko Bertogna. 2023-12-22. A Tricycle Model to Accurately Control an Autonomous Racecar with Locked Differential. https://doi.org/10.1109/icsc58660.2023.10449744

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