Neural adaptive control with unmodelled dynamic compensation

Abstract In this paper we present a new neuro controller. This controller is composed by two parts: neuro identifier and neuro controller. A single layer dynamic neural network is used as the identifier, then based on the identifier a direct linearization controller is applied. As the neural network cannot model the nonlinear system exactly, four different approaches are used to compensate the unmodelled dynamics.