An Modified Approximate Internal Model-based Neural Control

An modification of the Approximate Internal Modelbased Neural Control (AIMNC), using Multi Layer Perceptron (MLP) neural networks is proposed. A necessary condition that the system provides zero steady-state error for a constant reference and constant disturbances is derived. In the proposed control strategy only one neural network, which is the neural model of the plant, needs to be trained off-line. An inverse neural controller can be directly obtained from the neural model without need for a further training. Simulations demonstrate performance improvement of the proposed AIMNC modification. Keywords-input-output approximation; neural networks; nonlinear internal model control; zero steady-state error;