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.
