Disturbance rejection in nonlinear systems using neural networks

Neural networks with different architectures have been used for the identification and control of a wide class of nonlinear systems. In the present work, the authors consider the problem of input disturbance rejection, when such networks are used in practical problems. A large class of disturbances, which can be modeled as the outputs of unforced linear or nonlinear dynamic systems, are treated. The objective is to determine the identification model and the control law to minimize the effect of the disturbance at the output. In all cases, the method used involves expansion of the state space of the disturbance-free plant in an attempt to eliminate the effect of the disturbance. Several stages of increasing complexity of the problem are discussed in detail. Two simulation studies based on the results discussed are presented