Abstract
In the paper a novel method of prediction generation, based on fuzzy Hammerstein models, is proposed. Using this method one obtains the prediction described by analytical formulas. The prediction has such a form that the MPC (Model Predictive Control) algorithm utilizing it can be formulated as a numerically efficient quadratic optimization problem. At the same time, the algorithm offers practically the same performance as the MPC algorithm in which a nonlinear, non–convex optimization problem must be solved at each iteration. It is demonstrated in the control system of the distillation column – a nonlinear control plant with significant time delay.
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Marusak, P.M. (2010). On Prediction Generation in Efficient MPC Algorithms Based on Fuzzy Hammerstein Models. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2010. Lecture Notes in Computer Science(), vol 6113. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13208-7_18
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DOI: https://doi.org/10.1007/978-3-642-13208-7_18
Publisher Name: Springer, Berlin, Heidelberg
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