Feedforward Controller for Nonlinear Systems Utilizing a Genetically Trained Fuzzy Neural Network

Abstract

This paper presents an intelligent controller that acts as a FeedForwardController (FFC). utilizing the benefits of Fuzzy Logic (FL), Neural Networks(NNs) and Genetic Algorithms (GAs), this controller is built to controlnonlinear plants, where the GA is used to train this Fuzzy Neural Controller(FNC) by adjusting of its parameters based on minimizing the Mean Squareof Error (MSE) criterion.These parameters of the FNC include the input and output scaling factors,the centers and widths of the membership functions (MFs) for the inputvariable and the quantisation levels of the output variable, that are subjectedto constraints on their values by the expert. The GA used in this work is areal-coding GA with hybrid selection method and elitism strategy. To showthe effectiveness of this FNC several invertable (open-loop stable) nonlinearplants have been selected to be controlled by this FNC through simulation.