Feedforward controller for nonlinear systems utilizing a genetically trained fuzzy neural network

Abstract EN

This paper presents an intelligent controller that acts as a Feed Forward Controller (FFC).

Utilizing the benefits of Fuzzy Logic (FL), Neural Networks (NNs) and Genetic Algorithms (GAs), this controller is built to control nonlinear plants, where the GA is used to train this Fuzzy Neural Controller (FNC) by adjusting of its parameters based on minimizing the Mean Square of 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 input variable and the quantization levels of the output variable, that are subjected to constraints on their values by the expert.

The GA used in this work is a real-coding GA with hybrid selection method and elitism strategy.

To show the effectiveness of this FNC several invertible (open-loop stable) nonlinear plants have been selected to be controlled by this FNC through simulation.