Nonlinear predictive control based on the extraction of step-response models from Takagi-Sugeno fuzzy systems

Fischer, Martin; Schmidt, Michal; Kavšek-Biasizzo, Katarina

doi:10.1109/acc.1997.611983

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Fuzzy Model Predictive Control1

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“…Fuzzy sets can be applied in several ways in the context of MPC, e.g., at the modeling level (Sousa et ai., 1997;Nakamori, 1994;Pottrnann and Seborg, 1997;Fischer et al, 1997;de Oliveira and Lemos, 1995;Roubos et ai., 1998), in optimization (Lu et ai., 1997, and in the specification of the control objectives (Kaymak et ai., 1997). Since a model of the process is a part of the control scheme, this model can be adapted on-line in order to minimize the difference between the expected and real process outputs.…”

Section: Fuzzy Model Predictive Controlmentioning

confidence: 99%