DISTURBANCE REJECTION IN NONLINEAR SYSTEMS USING NEURO-FUZZY MODEL

The problem of disturbance rejection in the control of nonlinear systems with additive disturbance generated by some unforced nonlinear systems, was formulated and solved by {\itshape Mukhopadhyay} and {\itshape Narendra}, they applied the idea of increasing the order of the system, using neural networks the model of multilayer perceptron on several systems of varying complexity, so the objective of this work is using the same idea with two other recent methods fast and reliable ; fuzzy set systems and hybrid neuro-fuzzy systems respectively to compute the control law which minimizes the effect of the disturbance at the output of nonlinear systems. The application of the methods previously cited in form of results is presented to determine the identification model and to provide theoretical justification to existence a solution of disturbance rejection. Our better results with fuzzy systems and neuro-fuzzy systems are presented and discussed in detail in this paper with several systems of increasing complexity.