@inproceedings{f7c8cd8283364c1082c5f46786af4e0b,
title = "Generalized SVD reduced-order observers for Nonlinear systems",
abstract = "A nonlinear observer design method is proposed for the reduced order observation of nonlinear systems in the presence of sensor and process noise. Supernumerary sensors to the measured states are assumed to be available. State variables unavailable for observation by measurement are estimated with the proposed observer structure that requires lower computation than full order observers. By modeling output measurements as a generalized linear combination of observable states and measurement noise, this method combines generalized singular value decomposition (GSVD) static estimation of noisy output measurement and reduced order observer theory for estimating unmeasured state variables in nonlinear systems. This relatively low computation alternative to full-order observation can be of economic advantage in model predictive control applications.",
author = "Dada, {Gbolahan P.} and Antonios Armaou",
note = "Funding Information: 2 A. Armaou (senior member IEEE & AIChE) is with the Departments of Chemical Engineering and of Mechanical Engineering, the Pennsylvania State University, University Park, PA 16802 and the College of Mechanical and Electrical Engineering, Wenzhou University. Corresponding author, email: armaou@psu.edu *Financial support from the Ministry of Science & Technology of P.R.C. Award S2016G9027 is gratefully acknowledged. Publisher Copyright: {\textcopyright} 2020 AACC.; 2020 American Control Conference, ACC 2020 ; Conference date: 01-07-2020 Through 03-07-2020",
year = "2020",
month = jul,
doi = "10.23919/ACC45564.2020.9148000",
language = "English (US)",
series = "Proceedings of the American Control Conference",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "3473--3478",
booktitle = "2020 American Control Conference, ACC 2020",
address = "United States",
}