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

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3 Author(s)
M. Fischer ; Inst. of Autom. Control, Tech. Univ. of Darmstadt, Germany ; M. Schmidt ; K. K. Kavsek-Biasizzo

This paper deals with nonlinear predictive control based on higher order Takagi-Sugeno fuzzy systems which can also be interpreted as generalized radial basis function networks. We investigate how the fuzzy models can be linked to a special type of model based predictive control algorithm, namely the dynamic matrix control (DMC). Previously, purely linear step response models were used for long-range prediction. Here, the method is extended to nonlinear processes. Therefore, various step responses for different operating points are extracted from the fuzzy model. For performance evaluation, a heat exchanger is identified by means of the local linear model tree algorithm and controlled by the modified DMC

Published in:

American Control Conference, 1997. Proceedings of the 1997  (Volume:5 )

Date of Conference:

4-6 Jun 1997