Dynamic Performance of Estimator-based Speed Sensorless Control of Induction Machines Using Extended and Unscented Kalman Filters

Abstract

This paper presents an estimator-based speed sensorless field-oriented control (FOC) method for induction machines, where the state estimator is based on a self-contained, non-linear model. This model characterises both the electrical and the mechanical behaviours of the machine and describes them with seven state variables. The state variables are estimated from the measured stator currents and from the known stator voltages by using an estimator algorithm. An important aspect is that one of the state variables is the load torque and, hence, it is also estimated by the estimator. Using this feature, the applied estimator-based speed sensorless control algorithm may be operated adequately besides varying load torque. In this work, two different variants of the control algorithm are developed based on the extended and the unscented Kalman filters (EKF, UKF) as state estimators. The dynamic performance of these variants is tested and compared using experiments and simulations. Results show that the variants have comparable performance in general, but the UKF-based control provides better performance if a stochastically varying load disturbance is present.

Model-based control algorithm development of induction machines by using a well-defined model architecture and rapid control prototyping

“…The authors also plan to use the presented development approach in various future research projects. For example, an application may be found already in [49], where Kalman filters have been added to the control component library as new components and then utilised in the model layer for speed sensorless state estimation of the induction machine.…”

State space modeling theory of induction machines

“…where Q VWXY is the mechanical rotating speed, ω r is the electrical rotating speed of the rotor, J is the collective moment of inertia of the rotor, T L is the actual load and D is the viscous friction coefficient. The value of D is taken from [13]. The basic state space model can be obtained from (17), (19) and (20) [12]:…”

Stator Current-Based Model Reference Adaptive Control for Sensorless Speed Control of the Induction Motor

“…However, the accuracy is not very good due to the great sensitivity to motor parameter variation. In [8], the state estimator for the induction motor was based on the extended Kalman filter. e estimation of rotor speed is by using nonlinear state estimation and is more robust to the IM parameter changes or identification errors but much more complicated in practical realization.…”