Data-Driven Approach for Uncertainty Propagation and Reachability Analysis in Dynamical Systems
Abstract
In this paper, we propose a data-driven approach for uncertainty propagation and reachability analysis in a dynamical system. The proposed approach relies on the linear lifting of a nonlinear system using linear Perron-Frobenius (P-F) and Koopman operators. The uncertainty can be characterized in terms of the moments of a probability density function. We demonstrate how the P-F and Koopman operators are used for propagating the moments. Time-series data is used for the finite-dimensional approximation of the linear operators, thereby enabling data-driven approach for moment propagation. Simulation results are presented to demonstrate the effectiveness of the proposed method.
Keywords
Cite
@article{arxiv.2001.07668,
title = {Data-Driven Approach for Uncertainty Propagation and Reachability Analysis in Dynamical Systems},
author = {Amarsagar Reddy Ramapuram Matavalam and Umesh Vaidya and Venkataramana Ajjarapu},
journal= {arXiv preprint arXiv:2001.07668},
year = {2020}
}
Comments
Accepted in the 2020 American Control Conference, to be held in Denver, CO, USA on July 1-3, 2020