Abstract
Neurofuzzy networks offer an alternative approach both for the identification and the control of nonlinear processes in process engineering. The lack of software tools for the design of controllers based on hybrid neural networks and fuzzy models is particularly pronounced in this field. MODELICA is an oriented-object environment widely used which allows system-level developers to perform rapid prototyping and testing. Such programming environment offers an intuitive approach to both adaptive modeling and control in a great variety of engineering disciplines. In this paper we have developed an oriented-object model of binary distillation column with nonlinear dynamics, and an ANFIS (Adaptive-Network-based Fuzzy Inference System) neurofuzzy scheme has been applied to derive both an identification model and a adaptive controller to regulate distillation composition. The results obtained demonstrate the effectiveness of the neurofuzzy control scheme when the plant’s dynamics is given by a set of nonlinear differential algebraic equations (DAE).
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de Canete, J.F., Garcia-Cerezo, A., Garcia-Moral, I., del Saz, P., Ochoa, E. (2012). Object-Oriented Neurofuzzy Modeling and Control of a Binary Distillation Column by Using MODELICA. In: Jayne, C., Yue, S., Iliadis, L. (eds) Engineering Applications of Neural Networks. EANN 2012. Communications in Computer and Information Science, vol 311. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32909-8_6
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DOI: https://doi.org/10.1007/978-3-642-32909-8_6
Publisher Name: Springer, Berlin, Heidelberg
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