On-line training algorithms for an induction motor stator flux neural observer

Abstract

This work presents a neural network based stator flux observer. Although the network topology is a standard multilayer perceptron network, the training algorithms are new. This paper presents two on-line training algorithms, which are based on Variable Structure Systems (VSS) theory and Sliding Mode Control (SMC). The resulting observer shows good convergence velocity and robustness with respect to the induction motor parameters for both training algorithms tested.