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This study introduces a minimum-lap-time planning (MLTP) framework tailored for motorsport that enhances robustness against state disturbances and parameter uncertainties. By extending a prior disturbance-aware framework to incorporate uncertainties in vehicle parameters, the researchers implement a parsimonious activation strategy to ensure computational efficiency while maintaining critical robust constraints. The results demonstrate that robust planning significantly improves driveability and reduces failure rates in simulated driving scenarios, even at a moderate cost to sector time.
Robust planning strategies can lead to fewer failed runs and improved vehicle trackability in motorsport applications, even under significant disturbances and parameter uncertainties.
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.