Many problems in decision making, monitoring, fault detection, and
control require the knowledge of state variables and time-varying
parameters that are not directly measured by sensors. In such
situations, observers, or estimators, can be employed that use the
measured input and output signals along with a dynamic model of the
system in order to estimate the unknown states or parameters. An
essential requirement in designing an observer is to guarantee the
convergence of the estimates to the true values or at least to a small
neighborhood around the true values. However, for nonlinear,
large-scale, or time-varying systems, the design and tuning of an
observer is generally complicated and involves large computational
costs. This book provides a range of methods and tools to design
observers for nonlinear systems represented by a special type of a
dynamic nonlinear model -- the Takagi--Sugeno (TS) fuzzy model. The TS
model is a convex combination of affine linear models, which facilitates
its stability analysis and observer design by using effective algorithms
based on Lyapunov functions and linear matrix inequalities.
Takagi--Sugeno models are known to be universal approximators and, in
addition, a broad class of nonlinear systems can be exactly represented
as a TS system. Three particular structures of large-scale TS models are
considered: cascaded systems, distributed systems, and systems affected
by unknown disturbances. The reader will find in-depth theoretic
analysis accompanied by illustrative examples and simulations of
real-world systems. Stability analysis of TS fuzzy systems is addressed
in detail. The intended audience are graduate students and researchers
both from academia and industry. For newcomers to the field, the book
provides a concise introduction dynamic TS fuzzy models along with two
methods to construct TS models for a given nonlinear system
