A Universal Gains Selection Method for Speed Observers of Induction Machine

Abstract

Properties of state observers depend on proper gains selection. Each method of state estimation may require the implementation of specific techniques of finding those gains. The aim of this study is to propose a universal method of automatic gains selection and perform its verification on an induction machine speed observer. The method utilizes a genetic algorithm with fitness function which is directly based on the impulse response of the observer. System identification using least-squares estimation is implemented to determine the dynamic properties of the observer based on the estimation error signal. The influence of sampling time as well as signal length on the system identification has been studied. The results of gains selection using the proposed method have been compared with results obtained using the approach based on the placement of the poles of linearized estimation error equations. The introduced method delivers results comparable with analytical methods and does not require prior preparation specific to the implemented speed observer, such as linearization.

Fast Real-Time RDFT- and GDFT-Based Direct Fault Diagnosis of Induction Motor Drive

“…The assumed control system for the variable speed induction motor drive is based on the multiscalar control. An advantage of the multiscalar control versus Field-Oriented Control (FOC)is related to the fact that the representation of the variables is independent of the adopted reference frame in the mathematical model, as reported in [32].…”

“…To describe the dynamics of the object for the observer's needs, the induction machine model with the stator current vector and the rotor flux vector assumed as state variables is sufficient. The state observer synthesis based on the extended model for an induction machine as reported in [32][33][34] is performed by applying the following mathematical representation [33]:…”