Parsimonious disturbance-aware minimum-time planning with parametric uncertainty

• URL: http://arxiv.org/abs/2607.13312v1
• Date: Tue, 14 Jul 2026 22:32:35 GMT
• Title: Parsimonious disturbance-aware minimum-time planning with parametric uncertainty
• Abstract summary: This study presents and validates a minimum-lap-time planning framework for motorsport applications.<n>The methodology builds upon a prior disturbance-aware framework that propagates vehicle dynamics over a short horizon.<n>We extend the formulation to account for uncertainty in key vehicle parameters.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This study presents and validates a minimum-lap-time planning (MLTP) framework for motorsport applications that embeds robustness against both state disturbances and parameter uncertainty. The methodology builds upon a prior disturbance-aware framework that, at each track point, propagates stochastic vehicle dynamics over a short horizon and tightens tyre-friction constraints based on the worst-case scenario at horizon end. We extend the formulation to account for uncertainty in key vehicle parameters: moment of inertia, centre-of-mass position, and aerodynamic drag coefficient. To keep the extended formulation computationally tractable, a spatially selective, parsimonious activation strategy confines the robust constraints to the circuit segments where they are most critical. We demonstrate the improved driveability of the robust references by employing a model predictive controller (MPC) as a virtual test driver. For each reference, the same MPC drives a simulated FSAE (Formula SAE) car over 1000 runs on a representative Barcelona-Catalunya sector, with randomly realised impulsive disturbances and parameter scatter. We compare a nominal reference, planned without robustness, against its robust counterparts. The latter yield consistently fewer failed runs and, at a moderate sector-time cost, show tighter dispersion of key signals (vehicle inputs, axle saturations) around the reference values, evidence of better trackability.

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