Perspectives of Multivariable Fuzzy Control

The situation and trends in the application of fuzzy logic to control multi-input/multi-output (MIMO) systems are analyzed. The basic steps in designing a control system are considered. Fuzzy control applications are either knowledge-based or model-based. In model-based approaches, the first step is process modeling: Usually, interpolation and universal approximation are the two main features to be used in fuzzy MIMO systems in order to obtain a fuzzy function approximator which represents the controlled plant to a desired accuracy. For instance, the well known Takagi-Sugeno (T-S) model of the plant can be obtained. This leads to an integrated model which incorporates one “local behavior” in each fuzzy rule. Another alternative is following a reasoning approach. In this case, a MIMO plant is treated as a whole, with as many variables as required, leading to a complex set of rules and knowledge. In this case, also an integral model of the plant is obtained. The structure of the plant can be captured if the modeling approach deals with single input-output relationships and interactions among individual variables. In this case, a particular model can be AbstrAct