Rule-based fuzzy modeling has been recognised as a powerful technique for the modeling of partly-known nonlinear systems. Fuzzy models can effectively integrate information from different sources, such as physical laws, empirical models, measurements and heuristics. Application areas of fuzzy models include prediction, decision support, system analysis, control design, etc. Fuzzy Modeling for Control addresses fuzzy modeling from the systems and control engineering points of view. It focuses on the selection of appropriate model structures, on the acquisition of dynamic fuzzy models from process measurements (fuzzy identification), and on the design of nonlinear controllers based on fuzzy models. To automatically generate fuzzy models from measurements, a comprehensive methodology is developed which employs fuzzy clustering techniques to partition the available data into subsets characterized by locally linear behaviour. The relationships between the presented identification method and linear regression are exploited, allowing for the combination of fuzzy logic techniques with standard system identification tools. Attention is paid to the trade-off between the accuracy and transparency of the obtained fuzzy models. Control design based on a fuzzy model of a nonlinear dynamic process is addressed, using the concepts of model-based predictive control and internal model control with an inverted fuzzy model. To this end, methods to exactly invert specific types of fuzzy models are presented. In the context of predictive control, branch-and-bound optimization is applied. The main features of the presented techniques are illustrated by means of simple examples. In addition, three real-world applications are described. Finally, software tools for building fuzzy models from measurements are available from the author.
Robert Babuska was born in 1967 in Prague, Czechoslovakia. He received the M.Sc. degree (with honors) in Electrical Engineering from the Czech Technical University Prague (1990), and the Ph.D. degree (cum laude) in Control from the Delft University of Technology (1997). He has had faculty appointments at the Technical Cybernetics Department of the Czech Technical University Prague (1991-1993) and at the Faculty of Information Technology and Systems of the Delft University of Technology (1993-2003). In 1992 he spent six months as a visiting research scientist at Laboratoire d'Automatique de Grenoble, France. Currently he is Antoni van Leeuwenhoek professor at the Delft Center for Systems and Control, Delft University of Technology.
His research interests include fuzzy systems for modeling and identification, data-driven construction and adaptation of neuro-fuzzy systems, multiple-model approaches to modeling and control, fuzzy clustering for data analysis and learning control. He is active in applying these techniques in the areas robotics, mechatronics, aerospace and process control. Robert Babuska has co-authored over 340 publications, including one research monograph, two edited books and more than 70 journal papers.
