Abstract
Predictive Control is one of the most powerful techniques in process control, but its application in non-linear processes is challenging. This is basically because the optimization method commonly used limits the kind of functions which can be minimized. The aim of this work is to show how the combination of Genetic Algorithms (GA) and Generalized Predictive Control (GPC), what we call GAGPC, can be applied to nonlinear process control. This paper also shows GAGPC performance when controlling non-linear processes with model uncertanties. Success in this area will open the door to using GAGPC for a better control of industrial processes.
Supported by the Spanish Government Commission CICYT project TAP96-1090-C04-02.
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© 1998 Springer-Verlag
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Blasco, X., Martinez, M., Senent, J., Sanchis, J. (1998). Generalized predictive control using genetic algorithms (GAGPC). An application to control of a non-linear process with model uncertainty. In: Mira, J., del Pobil, A.P., Ali, M. (eds) Methodology and Tools in Knowledge-Based Systems. IEA/AIE 1998. Lecture Notes in Computer Science, vol 1415. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-64582-9_773
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DOI: https://doi.org/10.1007/3-540-64582-9_773
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