States and Parameters Estimation for Induction Motors Based on a New Adaptive Moving Horizon Estimation

Abstract

This paper investigates the joint states and parameters estimation problem for induction machine. In order to develop new states and parameters estimation methods that greatly improve the estimation bandwidth, this paper proposes an adaptive moving horizon estimation of the crucial states and parameters of the induction machine. The model of the machine under study is the one taking into consideration the magnetic saturation and the iron losses simultaneously. The estimator used is based on a least squares algorithm but includes a dead zone that ensures robustness and a variable forgetting factor that is based on the constant information principle. The simulation results show that the adaptive estimator can efficiently estimate the states and parameters of the induction machine with a fast convergence rate despite the initial parametric errors.