Neural Network Observer for Nonlinear Systems Application to Induction Motors

Abstract

In this paper, we investigate the problem of Neural Network (NN) observer for nonlinear systems. Therefore, it can be applied to systems with higher degree of nonlinearity with any a priory knowledge about system dynamics. The proposed neuro-observer is a three-layer feedforward neural network, which is trained extensively with the error backpropagation learning algorithm including a correction term to guarantee good tracking as well as bounded NN weights. Furthermore, the Lyapunov’s direct method is used in order to ensuring the stability of the proposed non-conventional observer and of the NN weight errors. The effectiveness of the proposed state observer scheme is demonstrated through numerical simulation to reconstruct the unavailable state variables of an induction motor (IM) and especially the rotor flux despite the effect of the arisen parameters such as the load torque which is also reconstructed using the NN observer.