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Higher-Order Approximation of Exit Functionals in Sampling-Based Stochastic Model Predictive Control

arXiv · AI, language, vision and robotics · article · Sep 21, 2026 · UTC

Safety evaluation in sampling-based stochastic model predictive control often requires numerical estimation of exit functionals. The approximation of first-exit times and exit indicators is therefore a key numerical bottleneck, and discretization error in these quantities directly affects the resulting controller. This paper studies how existing higher-order methods for strong approximation of exit times can be brought into safe control. Two cases are highlighted. For general noncommutative dynamics, an adaptive order-1 Milstein discretization is used together with Lévy-area simulation via Wik

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