Robust direct adaptive controller for a class of nonlinear systems based on neural networks and fuzzy logic system
In this paper a direct adaptive control algorithm based on a neural network NN as controller and a fuzzy inference system FIS as control error estimation is presented for a class of SISO uncertain nonlinear systems. The Weights adaptation laws are based on the control error. A fuzzy inference system is used to provide an estimate of this error based on past history of the system behavior. The stability of the closed loop is studied using Lyapunav theory. Simulation results demonstrate the effectiveness of the proposed approach.
adaptive control non linear control neural network control fuzzy systems