@inproceedings{c73d7aad9ff84cf1bc413ffe872066e5,
title = "Set-Valued State Estimation for Nonlinear Systems Using Hybrid Zonotopes",
abstract = "This paper proposes a method for set-valued state estimation of nonlinear, discrete-time systems. This is achieved by combining graphs of functions representing system dynamics and measurements with the hybrid zonotope set representation that can efficiently represent nonconvex and disjoint sets. Tight over-approximations of complex nonlinear functions are efficiently produced by leveraging special ordered sets and neural networks, which enable computation of set-valued state estimates that grow linearly in memory complexity with time. A numerical example demonstrates significant reduction of conservatism in the set-valued state estimates using the proposed method as compared to an idealized convex approach.",
author = "Siefert, {Jacob A.} and Thompson, {Andrew F.} and Glunt, {Jonah J.} and Pangborn, {Herschel C.}",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 62nd IEEE Conference on Decision and Control, CDC 2023 ; Conference date: 13-12-2023 Through 15-12-2023",
year = "2023",
doi = "10.1109/CDC49753.2023.10383789",
language = "English (US)",
series = "Proceedings of the IEEE Conference on Decision and Control",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2172--2177",
booktitle = "2023 62nd IEEE Conference on Decision and Control, CDC 2023",
address = "United States",
}