Applications

Abstract

This chapter describes three selected real-world applications of the fuzzy modeling, identification and control methods presented in this book. Section 7.1 describes the development of a knowledge-based linguistic fuzzy model for predicting the performance and tool wear of a rock-excavation machine. The model has been developed purely on basis of expert knowledge, but it allows for tuning and adjustment by using numerical data. Different facets of the approach, such as translation of the expert knowledge into the linguistic rules and membership functions, the hierarchical organization of knowledge, and validation of the model are discussed. Section 7.2 deals with nonlinear identification based on fuzzy clustering. Takagi-Sugeno, singleton and relational fuzzy models have been developed to model highly nonlinear pressure dynamics. Experimental results of real-time predictive control based on the fuzzy model are presented. The last application, given in Section 7.3, demonstrates a modeling approach based on the combination of a fuzzy model with a first-principles model. A linguistic fuzzy model which represents the kinetic term in enzymatic Penicillin-G conversion is developed from numerical data. This fuzzy model is then incorporated in macroscopic balance equations describing the overall conversion process. It is shown that this approach gives good predictions, and at the same time allows for qualitative interpretation of the unknown relationships learnt from data.