Author
Abstract
This paper presents an intelligent controller that acts as a FeedForward
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 quantisation 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 invertable (open-loop stable) nonlinear
plants have been selected to be controlled by this FNC through simulation
Keywords
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