Tuning of proportional plus derivative fuzzy logic controller using neural network

Abstract

The transformation of an expert's knowledge to control rules in a fuzzy logic controller has not been formalized and arbitrary choices concerning, for example, the shape of membership functions have to be made. The quality of a fuzzy controller can be drastically affected by the choice of membership functions. Thus, methods for tuning fuzzy logic controllers are needed. In the paper, neural networks and fuzzy logic are combined to solve the problem of tuning fuzzy logic controllers. The neurofuzzy controller uses the neural network learning techniques to tune the membership functions while keeping the semantics of the fuzzy logic controller intact. Both the architecture and the tuning algorithm are presented for a general neuro-fuzzy controller. From this, a procedure to tune a proportional plus derivative fuzzy controller is obtained. The algorithm for off-line tuning of the fuzzy controller is demonstrated with a numerical example.