Abstract
In this work a simulated wastewater treatment plant is controlled with a sliding mode control carried out with softcomputing techniques. The controller has two modules: the first one performs the plant control when its dynamics lies inside an optimal working region and is carried out by a neural network trained to reproduce the behavior of the technician who controls an actual plant, while the second one drives the system dynamics towards that region when it works outside it and is carried out by a corrective function whose parameters have been adjusted with a genetic algorithm. The controller so defined performs satisfactory even when extreme inputs are presented to the model.
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Jaramillo-Morán, M.A., Peguero-Chamizo, J.C., Martínez de Salazar, E., García del Valle, M. (2007). Sliding Mode Control of a Wastewater Plant with Neural Networks and Genetic Algorithms. In: Borrajo, D., Castillo, L., Corchado, J.M. (eds) Current Topics in Artificial Intelligence. CAEPIA 2007. Lecture Notes in Computer Science(), vol 4788. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75271-4_13
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DOI: https://doi.org/10.1007/978-3-540-75271-4_13
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