in this paper, a neural network model reference adaptive system speed observer is designed, which can be used in speed control of linear induction motors (lims). dynamical equations of lim have been considered accurate. in other words, the end effect and the electrical losses of the motor have been included in the motor equivalent circuit. then equations of the reference model and adaptive mode...

In this paper, a novel stator current based Model Reference Adaptive System (MRAS) estimator for speed estimation in the speed-sensorless vector controlled induction motor drives is presented. In the proposed MRAS estimator, measured stator current of the induction motor is considered as a reference model. The estimated stator current is produced in an adjustable model to compare with the measu...

In this paper, a neural network model reference adaptive system speed observer is designed, which can be used in speed control of linear induction motors (LIMs). Dynamical equations of LIM have been considered accurate. In other words, the end effect and the electrical losses of the motor have been included in the motor equivalent circuit. Then equations of the reference model and adaptive mode...

− The Model Reference Adaptive System (MRAS) is probably the most widely applied speed sensorless drive control scheme. This paper compares induction motor speed estimation using conventional MRAS and AI-based MRAS with Stator Resistance Compensation. A conventional mathematical model based MRAS speed estimation scheme can give a relatively precise speed estimation result, but errors will occur...

This paper proposes a new sliding mode control (SMC)  based model reference adaptive system (MRAS) for sensorless indirect field oriented control (IFOC) of a single-sided linear induction motor (SLIM). The operation of MRAS speed estimators dramatically depends on adaptation mechanism. Fixed-gain PI controller is conventionally used for this purpose which may fail to estimate the speed correctl...

This paper suggests novel sensorless speed estimation for an induction motor (IM) based on a stator current model reference adaptive system (IS-MRAS) scheme. The IS-MRAS scheme uses the error between the reference and estimated stator current vectors and the rotor speed. Observing rotor flux and the speed estimating using the conventional MRAS technique is confronted with certain problems relat...

In this paper, based on feedback linearization control method and using a special PI (propotational integrator) regulator (IP) in combination with a feed-forward controller, a three-phase induction servo-drive is speed controlled. First, an observer is employed to estimate the rotor d and q axis flux components. Then, two input-output state variables are introduced to control the dynamics of to...

In this paper, based on feedback linearization control method and using a special PI (propotational integrator) regulator (IP) in combination with a feed-forward controller, a three-phase induction servo-drive is speed controlled. First, an observer is employed to estimate the rotor d and q axis flux components. Then, two input-output state variables are introduced to control the dynamics of to...

This paper presents a new algorithm based on Model Reference Adaptive System (MRAS) and its stability analysis for sensorless control of Doubly-Fed Induction Generators (DFIGs). The reference and adjustable models of the suggested observer are based on the active power of the machine. A hysteresis block is used in the structure of the adaptation mechanism, and the stability analysis is performe...

Rotor Flux based Model Reference Adaptive System (RF-MRAS) is the most popularly used conventional speed estimation scheme for sensor-less IM drives. In this scheme, the voltage model equations are used for the reference model. This encounters major drawbacks at low frequencies/speed which leads to the poor performance of RF-MRAS. Replacing the reference model using Neural Network (NN) based fl...