Design of a neural network observer to control a FED-batch bioprocess

Author

Boutalis, Y.S. ; Kosmidou, O.I.

Author_Institution

Dept. of Electr. & Comput. Eng., Democritus Univ. of Thrace, Xanthi, Greece

fYear

1999

fDate

Aug. 31 1999-Sept. 3 1999

Firstpage

1878

Lastpage

1883

Abstract

The present paper deals with the production of Saccharomyces cerevisiae, described by a sixth order nonlinear state space model. The control objective is to ensure the process stability and desirable specifications in the presence of disturbances and lack of reliable state measurements. First, the model of the process and its properties are presented. Next the ability of multi-layer Neural Networks to act as reliable emulators of the system dynamics is tested by simulation results and a training strategy is proposed to improve their performance. Finally, a non-linear adaptive observer is designed by means of artificial neural networks.

Keywords

adaptive control; batch processing (industrial); microorganisms; multilayers; neurocontrollers; nonlinear control systems; stability; state-space methods; FED-batch bioprocess control; Saccharomyces cerevisiae; artificial neural network; control objective; multilayer neural networks; neural network observer; nonlinear adaptive observer; process stability; sixth order nonlinear state space model; state measurement; system dynamics; training strategy; Artificial neural networks; Mathematical model; Observers; Reliability; Sugar; Training; Bioprocess; Neural Networks; State Observer;

fLanguage

English

Publisher

ieee

Conference_Titel

Control Conference (ECC), 1999 European

Conference_Location

Karlsruhe

Print_ISBN

978-3-9524173-5-5

Type

conf

Filename

7099590