Augmentation of an existing linear controller with an adaptive element

This paper describes an approach for augmenting an existing linear controller design with a neural network based adaptive element. The basic approach involves formulating an architecture for which the associated error equation has a form suitable for applying existing results for adaptive output feedback control of nonlinear processes. The approach is applicable to non-affine, nonlinear systems with both parametric uncertainty and unmodeled dynamics. There are no restrictions placed on the relative degree of the regulated output variable, and the uncertainties can be unmatched. New results related to disturbance cancellation in an adaptive context are presented. For simplicity, only the SISO case is treated. The overall approach is illustrated using a simple model for a flexible system.