Increasingly demanding performance requirements for dynamical systems motivates the adoption of nonlinear and adaptive control techniques. One challenge is the nonlinearity of the resulting closed-loop system complicates verification that the system does satisfy the requirements at all possible operating conditions. This paper presents a data-driven procedure for efficient simulation-based, statistical verification without the reliance upon exhaustive simulations. In contrast to previous work, this approach introduces a method for online estimation of prediction accuracy without the use of external validation sets. This work also develops a novel active sampling algorithm that iteratively selects additional training points in order to maximize the accuracy of the predictions while still limited to a sample budget. Three case studies demonstrate the utility of the new approach and the results show up to a 50% improvement over state-of-the-art techniques.
@article{arxiv.1705.01471,
title = {Active Sampling for Constrained Simulation-based Verification of Uncertain Nonlinear Systems},
author = {John F. Quindlen and Ufuk Topcu and Girish Chowdhary and Jonathan P. How},
journal= {arXiv preprint arXiv:1705.01471},
year = {2017}
}