New Model Reference Adaptive System Speed Observer for Field-Oriented Control Induction Motor Drives Using Neural Networks

Hossein Rahimi Khoei,Mahdi Zolfaghari

doi:10.11591/520

Abstract

New Model Reference Adaptive System Speed Observer for Field-Oriented Control Induction Motor Drives Using Neural Networks

Highlights

Induction motors are electromechanical systems suitable for a large spectrum of industrial applications, due to its high reliability, relatively low cost, and modest maintenance requirements [1]
Artificial Neural Network advantages such as: ability to approximate arbitrary nonlinear mappings learning from the real system or the approximate intelligence and self-organizing capability possibility of parallel computing robustness ability to generalize and fault tolerance It is a major advantage of ANN based techniques that they do not require any mathematical model of the motor under consideration and the drive development time can be substantially reduced [4]
Speed Estimation using Neural Network In Model Reference Adaptive System (MRAS) technique, some state variables, Xd, Xq of the induction machine are estimated in a reference model and are compared with state variables Xd, Xq estimated by using an adaptive model

Summary

Introduction

Induction motors are electromechanical systems suitable for a large spectrum of industrial applications, due to its high reliability, relatively low cost, and modest maintenance requirements [1]. They have estimated speed from the instantaneous values of stator voltages and currents using induction motor model.

Results

Conclusion