Radial Basis Function Controller of a Class of Nonlinear Systems Using Mamdani Type as a Fuzzy Estimator | Zendy

Mohamed Bahita | Zendy; Khaled Belarbi | Zendy

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Radial Basis Function Controller of a Class of Nonlinear Systems Using Mamdani Type as a Fuzzy Estimator

Author(s) -

Mohamed Bahita,

Khaled Belarbi

Publication year - 2012

Publication title -

procedia engineering

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.32

H-Index - 74

ISSN - 1877-7058

DOI - 10.1016/j.proeng.2012.07.204

Subject(s) - control theory (sociology) , radial basis function , controller (irrigation) , gradient descent , artificial neural network , fuzzy control system , adaptive neuro fuzzy inference system , radial basis function network , estimator , nonlinear system , linearization , computer science , fuzzy logic , mathematics , artificial intelligence , control (management) , statistics , physics , quantum mechanics , agronomy , biology

In this work we consider the application of an adaptive neural network control for a class of single input single output non linear systems. The method uses a neural network system of Radial Basis Function (RBF) type to approximate the feedback linearization law and a fuzzy inference system of Mamdani type to estimate the control signal error between the ideal unknown control signal and the actual control signal. The rule base of the Mamdani fuzzy system is constructed using simple expert reasoning. The parameters of the (RBF) controller are adapted and changed using the gradient descent law based on the estimated control error. The simulation is carried out on a three tanks system with the objective of controlling the level of one tank. The simulation results show that the proposed RBF-Mamdani scheme performs successful and robust control in comparison to the results obtained using a PI controller

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