Title: Optimal experiment design under process noise using riccati differential equations
Authors: Telen, Dries
Houska, Boris
Logist, Filip
Van Derlinden, Eva
Diehl, Moritz
Van Impe, Jan # ×
Issue Date: Jan-2013
Publisher: Butterworth-Heinemann
Series Title: Journal of Process Control vol:23 issue:4 pages:613-629
Abstract: In this paper, we present a numerical method for optimal experiment design of nonlinear dynamic processes. Here, we suggest to optimize an approximation of the predicted variance–covariance matrix of the parameter estimates, which can be computed as the solution of a Riccati differential equation. In contrast to existing approaches, the proposed method allows us to take process noise into account and requires less derivative states to be computed compared to the traditional Fisher information matrix based approach. This process noise is assumed to be a time-varying random disturbance which is not known at the time when the experiment is designed. We illustrate the technique by solving an optimal experiment design problem for a fed-batch bioreactor benchmark case study. Here, we concentrate on how the optimal input design and associated accuracy of the parameter identification is influenced when process noise is present.
ISSN: 0959-1524
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Bio- & Chemical Systems Technology, Reactor Engineering and Safety Section
ESAT - STADIUS, Stadius Centre for Dynamical Systems, Signal Processing and Data Analytics
× corresponding author
# (joint) last author

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