Abstract
Numerically efficient analytical MPC (Model Predictive Control) algorithm based on fuzzy Hammerstein models is proposed in the paper. Thanks to the form of the model the prediction can be described by analytical formulas and the proposed algorithm is numerically efficient. It is shown that thanks to a clever tuning of the controller most of calculations needed to derive the control value can be performed off–line. Thus, the proposed algorithm has the advantage reserved so far for analytical MPC algorithms based on linear models. At the same time, the algorithm offers practically the same performance as the MPC algorithm in which a nonlinear optimization problem must be solved at each iteration. The efficiency of the algorithm is demonstrated in the control system of a nonlinear control plant with delay.
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References
Babuska, R., te Braake, H.A.B., van Can, H.J.L., Krijgsman, A.J., Verbruggen, H.B.: Comparison of intelligent control schemes for real–time pressure control. Control Engineering Practice 4, 1585–1592 (1996)
Camacho, E.F., Bordons, C.: Model Predictive Control. Springer, Heidelberg (1999)
Fink, A., Fischer, M., Nelles, O., Isermann, R.: Supervision of nonlinear adaptive controllers based on fuzzy models. Control Engineering Practice 8, 1093–1105 (2000)
Janczak, A.: Identification of nonlinear systems using neural networks and polynomial models: a block–oriented approach. Springer, Heidelberg (2005)
Lawrynczuk, M.: A family of model predictive control algorithms with artificial neural networks. International Journal of Applied Mathematics and Computer Science 17, 217–232 (2007)
Maciejowski, J.M.: Predictive control with constraints. Prentice Hall, Harlow (2002)
Marusak, P.: Advantages of an easy to design fuzzy predictive algorithm in control systems of nonlinear chemical reactors. Applied Soft Computing 9, 1111–1125 (2009)
Marusak, P.: Efficient model predictive control algorithm with fuzzy approximations of nonlinear models. In: Kolehmainen, M., Toivanen, P., Beliczynski, B. (eds.) ICANNGA 2009. LNCS, vol. 5495, pp. 448–457. Springer, Heidelberg (2009)
Marusak, P.: 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.) ICAISC 2010. LNCS (LNAI), vol. 6113, pp. 136–143. Springer, Heidelberg (2010)
Morari, M., Lee, J.H.: Model predictive control: past, present and future. Computers and Chemical Engineering 23, 667–682 (1999)
Piegat, A.: Fuzzy Modeling and Control. Physica-Verlag, Berlin (2001)
Rossiter, J.A.: Model-Based Predictive Control. CRC Press, Boca Raton (2003)
Takagi, T., Sugeno, M.: Fuzzy identification of systems and its application to modeling and control. IEEE Trans. Systems, Man and Cybernetics 15, 116–132 (1985)
Tatjewski, P.: Advanced Control of Industrial Processes; Structures and Algorithms. Springer, London (2007)
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Marusak, P.M. (2011). Numerically Efficient Analytical MPC Algorithm Based on Fuzzy Hammerstein Models. In: Dobnikar, A., Lotrič, U., Šter, B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2011. Lecture Notes in Computer Science, vol 6594. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20267-4_19
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DOI: https://doi.org/10.1007/978-3-642-20267-4_19
Publisher Name: Springer, Berlin, Heidelberg
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