A new approach for the design of fuzzy controllers in real time

This paper presents a new methodology to achieve real time self tuning and self-learning in fuzzy controllers. The advantage of this approach is that it only requires qualitative information about the plant to be controlled, in terms of the monotony presented by the output with respect to the control signal and delays of the plant. Thus, it is capable of controlling highly nonlinear systems, in a pseudo-optimum way, even when these are time variable. Control is achieved by means of two auxiliary systems: the first one is responsible for adapting the consequences of the main controller to minimize the error arising at the plant output, while the second auxiliary system compiles real input/output data obtained from the plant. The system then learns from these data, adapting both the consequences of the rules and the parameters that define the membership functions, taking into account, not the current state of the plant but rather the global identification performed.