DISTURBANCE REJECTION IN NONLINEAR-SYSTEMS USING NEURAL NETWORKS

Neural networks with different architectures have been successfully used in recent years for the identification and control of a wide class of nonlinear systems. In this paper, we consider the problem of rejection of input disturbances, 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, is 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. Theoretical justification is provided for the existence of solutions to the problem of complete rejection of the disturbance in special cases. This provides the rationale for using similar techniques in situations where such theoretical analysis is not available. Examples in the form of simulation studies using both radial basis function networks and multilayer neural networks are presented throughout the paper to demonstrate the effectiveness of the methods suggested.