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Neural Computing and Applications

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01.03.2010 | KES 2008

Neural networks-based adaptive control for a class of nonlinear bioprocesses

verfasst von: Emil Petre, Dan Selişteanu, Dorin Şendrescu, Cosmin Ionete

Erschienen in: Neural Computing and Applications | Ausgabe 2/2010

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Abstract

The paper studies the design and analysis of a neural adaptive control strategy for a class of square nonlinear bioprocesses with incompletely known and time-varying dynamics. In fact, an adaptive controller based on a dynamical neural network used as a model of the unknown plant is developed. The neural controller design is achieved by using an input–output feedback linearization technique. The adaptation laws of neural network weights are derived from a Lyapunov stability property of the closed-loop system. The convergence of the system tracking error to zero is guaranteed without the need of network weights convergence. The resulted control method is applied in a depollution control problem in the case of a wastewater treatment bioprocess, belonging to the square nonlinear class, for which kinetic dynamics are strongly nonlinear, time varying and not exactly known.

Vorheriger Artikel Advances in design and application of neural networks

Nächster Artikel A fuzzy spatial coherence-based approach to background/foreground separation for moving object detection

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Titel: Neural networks-based adaptive control for a class of nonlinear bioprocesses
verfasst von: Emil Petre
Dan Selişteanu
Dorin Şendrescu
Cosmin Ionete
Publikationsdatum: 01.03.2010
Verlag: Springer-Verlag
Erschienen in: Neural Computing and Applications / Ausgabe 2/2010
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-009-0284-9

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