@inproceedings{2793c9776cb149f4adbb0bc01266457b,
title = "Active Sampling for Closed-Loop Statistical Verification of Uncertain Nonlinear Systems",
abstract = "Increasingly demanding performance requirements for dynamical systems motivate 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.",
author = "Quindlen, {John F.} and Ufuk Topcu and Girish Chowdhary and How, {Jonathan P.}",
note = "Publisher Copyright: {\textcopyright} 2018 AACC.; 2018 Annual American Control Conference, ACC 2018 ; Conference date: 27-06-2018 Through 29-06-2018",
year = "2018",
month = aug,
day = "9",
doi = "10.23919/ACC.2018.8431662",
language = "English (US)",
isbn = "9781538654286",
series = "Proceedings of the American Control Conference",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "6259--6265",
booktitle = "2018 Annual American Control Conference, ACC 2018",
address = "United States",
}