Sliding Mode Control of a Wastewater Treatment Plant with Neural Networks
In this work a sliding mode control carried out by neural networks and applied to a wastewater treatment plant is proposed. 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 another neural network trained to perform that task. Both controllers are combined with a two layers neural network where the synaptic weights of the only neuron in the second one is adjusted by those in the previous layer in order to balance the contribution of each controller to the total control action.
KeywordsNeural Network Wastewater Treatment Plant Synaptic Weight Aeration Tank Cellular Neural Network
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