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

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.