SinceinitiatedbyLot?A. Zadehin1965, fuzzysettheoryhastriggeredacons-
erably large body of areas to blossom. A fuzzy system is, in a very
broad sense, anyfuzzylogic-basedsystemwherefuzzylogiccanbeusedeither
asthebasisfor the representation of di?erent forms of system knowledge
or the model for the interactions and relationships among the system
variables. Fuzzy systems have proven to be an important tool for
modeling complex systems for which, due to complexity or imprecision,
classical tools are unsuccessful. There have been diverse ?elds of
applications of fuzzy technology from medicine to management, from
engineering to behavioral science, from vehicle control to computational
linguistics, and so on. Fuzzy modeling is a conjunction to understand
the s- tem's behavior and build useful mathematical models. Di?erent
types of fuzzy models have been proposed in the literature, among which
the Takagi-Sugeno (T-S) fuzzy model is a rule-based one suitable for the
accurate approximation and identi?cation of a wide class of nonlinear
systems. There has been an - creasing amount of work on analysis and
synthesis of fuzzy systems based on T-S fuzzy models. Since 2000, T-S
fuzzy model approach has been extended to
tackleanalysisandcontrolproblemsofnonlinear systemswith time delay. So
far extensive results have been presented for investigating T-S fuzzy
systems with time delay, many of which adopt an easy and popular scheme,
say, linear matrix inequality (LMI) based method. However, there lacks
of a monograph in this direction to provide the state-of-the-art of
coverage of this new growing area.
