Closed-Loop Statistical Verification of Stochastic Nonlinear Systems Subject to Parametric Uncertainties
Name
1709.06645.pdf
Description
Accepted version
Size
361.67 KB
Format
Adobe PDF
Checksum (MD5)
97729b0f05441885035cce6852341d04
Author(s)
Quindlen, John F.
Topcu, Ufuk
Chowdhary, Girish
How, Jonathan P.
Date Issued
June 2018
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Quindlen, John F., Topcu, Ufuk, Chowdhary, Girish and How, Jonathan P. 2018. "Closed-Loop Statistical Verification of Stochastic Nonlinear Systems Subject to Parametric Uncertainties."
Version
Original manuscript
Abstract
© 2018 AACC. This paper proposes a statistical verification framework using Gaussian processes (GPs) for simulation-based verification of stochastic nonlinear systems with parametric uncertainties. Given a small number of stochastic simulations, the proposed framework constructs a GP regression model and predicts the system's performance over the entire set of possible uncertainties. Included in the framework is a new metric to estimate the confidence in those predictions based on the variance of the GP's cumulative distribution function. This variance-based metric forms the basis of active sampling algorithms that aim to minimize prediction error through careful selection of simulations. In three case studies, the new active sampling algorithms demonstrate up to a 35% improvement in prediction error over other approaches and are able to correctly identify regions with low prediction confidence through the variance metric.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.23919/ACC.2018.8431742
