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

  • KES 2008
  • Published:
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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.

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Acknowledgments

This work was created in the frame of the research project CNCSIS (National University Research Council) ID 548/2009, Romania.

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Authors and Affiliations

  1. Department of Automatic Control, University of Craiova, A.I. Cuza 13, Craiova, Romania

    Emil Petre, Dan Selişteanu, Dorin Şendrescu & Cosmin Ionete

Authors
  1. Emil Petre
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  2. Dan Selişteanu
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  3. Dorin Şendrescu
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  4. Cosmin Ionete
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Correspondence to Emil Petre.

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Petre, E., Selişteanu, D., Şendrescu, D. et al. Neural networks-based adaptive control for a class of nonlinear bioprocesses. Neural Comput & Applic 19, 169–178 (2010). https://doi.org/10.1007/s00521-009-0284-9

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  • DOI: https://doi.org/10.1007/s00521-009-0284-9

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