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Direct Adaptive Control of an Anaerobic Depollution Bioprocess Using Radial Basis Neural Networks

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Abstract

This work deals with the design and analysis of a nonlinear and neural adaptive control strategy for an anaerobic depollution bioprocess. A direct adaptive controller based on a radial basis function neural network used as on-line approximator to learn the time-varying characteristics of process parameters is developed and then is compared with a classical linearizing controller. The controller design is achieved by using an input-output feedback linearization technique. Numerical simulations, conducted in the case of a strongly nonlinear, time varying and not exactly known dynamical kinetics wastewater biodegradation process, are included to illustrate the behaviour and the performance of the presented controller.

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Petre, E., Şendrescu, D., Selişteanu, D. (2010). Direct Adaptive Control of an Anaerobic Depollution Bioprocess Using Radial Basis Neural Networks . In: Setchi, R., Jordanov, I., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based and Intelligent Information and Engineering Systems. KES 2010. Lecture Notes in Computer Science(), vol 6277. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15390-7_24

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  • DOI: https://doi.org/10.1007/978-3-642-15390-7_24

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15389-1

  • Online ISBN: 978-3-642-15390-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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