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Fuzzy model based control with use of a priori knowledge

Project members: R. Babuška, J. Abonyi (University of Veszprem, Hungary)

Effective development of nonlinear dynamic process models is of great importance in the application of model-based control. Typically, one needs to blend information from different sources: experience of operators and designers, process data and first principle knowledge formulated by mathematical equations. To incorporate a priori knowledge into data-driven identification of dynamic fuzzy models of the Takagi-Sugeno type a constrained identification algorithm has been developed, where the constrains on the model parameters are based on the knowledge about the process stability, minimal or maximal gain, and the settling time. The algorithm has been successfully applied to off-line and on-line adaptation of fuzzy models.

When no a priori knowledge about the local dynamic behavior of the process is available, information about the steady-state characteristic could be extremely useful. Because of the difficult analysis of the steady-state behavior of dynamic fuzzy models of the Takagi-Sugeno type, block-oriented fuzzy models have been developed. In the Fuzzy Hammerstein (FH) model, a static fuzzy model is connected in series with a linear dynamic model. The obtained FH model is incorporated in a model-based predictive control scheme. Results show that the proposed FH modeling approach is useful for modular parsimonious modeling and model-based control of nonlinear systems.

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