Abstract
A fuzzy predictive set–point optimizer which uses a nonlinear, fuzzy dynamic process model is proposed in the paper. The algorithm of the optimizer is formulated in such a way that only a numerically efficient, quadratic optimization problem must be solved at each algorithm iteration. It is demonstrated, using an example of a control system of a nonlinear MIMO control plant, that application of the optimizer based on a fuzzy model instead of a linear one can result in substantial improvement of control system operation. The fuzzy control plant model, the optimizer is based on, consists of local models in the form of control plant step responses. Thus, the model is easy to obtain and the proposed optimizer easy to design.
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Marusak, P.M. (2008). Efficient Fuzzy Predictive Economic Set–Point Optimizer. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing – ICAISC 2008. ICAISC 2008. Lecture Notes in Computer Science(), vol 5097. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69731-2_27
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DOI: https://doi.org/10.1007/978-3-540-69731-2_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69572-1
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