TY - GEN
T1 - Generalized SVD reduced-order observers for Nonlinear systems
AU - Dada, Gbolahan P.
AU - Armaou, Antonios
N1 - 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: [email protected] *Financial support from the Ministry of Science & Technology of P.R.C. Award S2016G9027 is gratefully acknowledged.
Publisher Copyright:
© 2020 AACC.
PY - 2020/7
Y1 - 2020/7
N2 - 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.
AB - 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.
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U2 - 10.23919/ACC45564.2020.9148000
DO - 10.23919/ACC45564.2020.9148000
M3 - Conference contribution
AN - SCOPUS:85089557329
T3 - Proceedings of the American Control Conference
SP - 3473
EP - 3478
BT - 2020 American Control Conference, ACC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 American Control Conference, ACC 2020
Y2 - 1 July 2020 through 3 July 2020
ER -