Tuning of proportional plus derivative fuzzy logic controller for nonlinear system control

Abstract

The transformation of 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 this paper, neural networks and fuzzy logic are combined to address the problem of tuning fuzzy logic controllers. The neuro-fuzzy controller uses neural network learning techniques to tune membership functions while keeping the semantics of the fuzzy logic controller intact. Nonlinear systems present a wide spectrum of challenges for control engineers. With neuro-fuzzy techniques, the opportunity exits to control nonlinear systems without the need for a precise mathematical model of the system under control. The architecture and tuning algorithm for a proportional plus derivative neurofuzzy logic controller (PDNFLC) is presented in this paper. This step-by-step algorithm for the off-line tuning of a feedforward PDNFLC is demonstrated by a numerical example.