Fuzzy Modeling For Control (international Series In Intelligent Technologies)
by Robert Babuska /
1998 / English / PDF
14.8 MB Download
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.
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
Fuzzy
Modeling forControl
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.
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.
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.
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.