Abstract

This paper presents a fuzzy modelling and tracking control methodology for complex systems by combining the merits of fuzzy logic and conventional linear control theory. Here, fuzzy logic is used to formulate a system model by aggregating a set of linearized local subsystems which identify the nonlinear system approximately, and a fuzzy feedback controller is designed by use of conventional linear feedback theory and fuzzy reasoning. A simulation example of a one-link rigid robotic manipulator is given to demonstrate the validity of the proposed control scheme. It is shown that the fuzzy model can be simplified, and good tracking control performance can be achieved by choosing appropriate fuzzy roles.

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