A neural-fuzzy logic approach for modeling and control of nonlinear systems

Neural networks and fuzzy logic systems are two of the most important results of the research in the area of soft computing. While neural networks and fuzzy logic have added a new dimension to many engineering fields of study, their weaknesses have not been overlooked, in many applications the training of a neural network requires a large amount of iterative calculations. The technique used in this work replaces the rule-base of a traditional fuzzy logic system with backpropagation neural network. We propose an adaptive neuro-fuzzy logic control scheme (ANFLC) based on the neural network learning capability and the fuzzy logic modeling ability. The development of this system is carried out in two phases: the first phase involves training a multilayer neuro-emulator (NE) for the forward dynamics of the plant to be controlled; and the second phase involves online learning of the neuro-fuzzy logic controller (NFLC). Extensive simulation studies of nonlinear dynamic systems are carried out to illustrate the effectiveness and applicability of the proposed scheme.