Model Reference Based Neural Network Adaptive Controller

Linear system theory has made significant contribute to the developments of the classical control´s area in the past three decades. The motivation of this paper emerges from the need to develop novel control strategies that can be applied to nonlinear dynamic systems. Furthermore, the need for an adaptive scheme emerges for dealing with time varying systems. Paper presents model reference based neural network structure that can be used for adaptive control of linear and nonlinear processes. The proposed neural network controller is tested on a simulated linear system.