Parsimonious disturbance-aware minimum-time planning with parametric uncertainty
For motorsport and autonomous racing, this provides a computationally feasible method to generate robust minimum-time trajectories that improve driveability under combined state and parameter uncertainty.
This work extends a disturbance-aware minimum-lap-time planning framework to handle parametric uncertainty in vehicle parameters, using a parsimonious activation strategy to maintain tractability. In 1000 simulated runs on a Barcelona-Catalunya sector, the robust plans reduced failure rates and tightened signal dispersion compared to a nominal plan, at a moderate time cost.
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.