It is known that many control processes are characterized by both
quantitative and qualitative complexity. Tbe quantitative complexity is
usually expressed in a large number of state variables, respectively
high dimensional mathematical model. Tbe qualitative complexity is
usually associated with uncertain behaviour, respectively approximately
known mathematical model. If the above two aspects of complexity are
considered separately, the corresponding control problem can be easily
solved. On one hand, large scale systems theory has existed for more
than 20 years and has proved its capabilities in solving high
dimensional control problems on the basis of decomposition, hierarchy,
decentralization and multilayers. On the other hand, the fuzzy
linguistic approach is almost at the same age and has shown its
advantages in solving approximately formulated control problems on the
basis of linguistic reasoning and logical inference. However, if both
aspects of complexity are considered together, the corresponding control
problem becomes non-trivial and does not have an easy solution. Modem
control theory and practice have reacted accordingly to the above
mentioned new cballenges of tbe day by utilizing the latest achievements
in computer technology and artificial intelligence distributed
computation and intelligent operation. In this respect, a new field has
emerged in the last decade, called " Distributed intelligent control
systems" . However, the majority of the familiar works in this field are
still either on an empirical or on a conceptual level and this is a
significant drawback.
