A Tricycle Model to Accurately Control an Autonomous Racecar with Locked Differential
This work addresses the specific challenge of precise control for autonomous racecars operating near tire limits, representing an incremental advancement in domain-specific robotics.
The authors tackled the problem of controlling an autonomous racecar with a locked differential by developing a novel tricycle model and a Model Predictive Controller with micro-steps discretization, resulting in improved lateral path tracking as demonstrated in experimental tests on a Dallara AV-21 at the Monza F1 racetrack.
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.