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P. Bogaerts, A. Vande Wouwer
Model Identification for State Estimation – Application to a Bioprocess
Based on a process model and some available measurements, state observers allow unmeasured states to be reconstructed on-line. When the underlying process model is established, the unknown parameters are usually identified by minimizing a least-squares or maximum likelihood criterion, which makes use of off-line measurements of the complete state vector. These conventional criteria do not express the condition that the model of the unmeasured part of the state should be sensitive to the measured one. In this study, a new cost function is proposed, which attempts to enforce a higher model sensitivity by combining a classical maximum likelihood criterion with a scalar measure of the model sensitivity.
at – Automatisierungstechnik, Oldenbourg Wissenschaftsverlag
Print ISSN: 0178-2312
Volume: 48, 05/2000
Pages: 240