An implementation of rotor speed observer for sensorless induction motor drive in case of machine parameter uncertainty

The paper describes observers using model reference adaptive system for sensorless induction motor drive with the pulse width modulator and the direct torque control under the circumstances of incorrect information of induction motor parameters. An approximation based on the definition of the Laplace transformation is used to obtain initial values of the parameters. These values are utilized to simulate sensorless control structures of the induction motor drive in Matlab-Simulink environment. Performance comparison of two typical observers is carried out at different speed areas and in presence of parameter uncertainty. A laboratory stand with the induction motor drive and load unit is set up to verify the properties of observers. Experimental results confirm the expected dynamic properties of selected observer

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

One of the primary advantages of field-oriented controlled induction motor for high performance application is the capability for easy field weakening and the full utilization of voltage and current rating of the inverter to obtain a wide dynamic speed rangeThis paper describes a Model Reference Adaptive System (MRAS) based scheme using Artificial Neural Network (ANN) for online speed estimation of sensorless vector controlled induction motor drive. The proposed MRAS speed observer uses the current model as an adaptive model. The neural network has been then designed and trained online by employing a back propagation network (BPN) algorithm. The estimator was designed and simulated in Matlab/Simulink. Simulation result shows a good performance of speed estimator. The simulation results show good performance in various operating conditions. Also Performance analysis of speed estimator with the change in resistances of stator is presented. Simulation results show this estimator robust to parameter variations especially resistances of stator

Machine Model Based Speed Estimation Schemes for Speed Encoderless Induction Motor Drives: a Survey

Speed Estimation without speed sensors is a complex phenomenon and is overly dependent on the machine parameters. It is all the more significant during low speed or near zero speed operation. There are several approaches to speed estimation of an induction motor. Eventually, they can be classified into two types, namely, estimation based on the machine model and estimation based on magnetic saliency and air gap space harmonics. This paper, through a brief literature survey, attempts to give an overview of the fundamentals and the current trends in various machine model based speed estimation techniques which have occupied and continue to occupy a great amount of research space

Three-Phase Induction Motor Speed Estimation Using Recurrent Neural Network

In induction motor speed control method, the development of the field-oriented control (FOC) algorithm which can control torque and flux separately enables the motor to replace many roles of DC motors. Induction motor speed control can be done by using a close loop system which requires a speed sensor. Referring to the speed sensor weaknesses such as less accurate of the measurement, this is due to the placement of the sensor system that is too far from the control system. Therefore, a speed sensorless method was developed which has various advantages. In this study, the speed sensorless method using an artificial neural network with recurrent neural network (RNN) as speed observer on three-phase induction motor has been discussed. The RNN can maintain steady-state conditions against a well-defined set point speed, so that the observer is able and will be suitable if applied as input control for the motor drives. In this work, the RNN has successfully estimated the rotor flux of the induction motor in MATLAB R2019a simulation as about 0.0004Wb. As based on speed estimation error, the estimator used has produced at about 26.77%, 8.7% and 6.1% for 150rad/s, 200rad/s and 250rad/s respectively. The future work can be developed and improved by creating a prototype system of the induction motor to get more accurate results in real-time of the proposed RNN observer

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

One of the primary advantages of field-oriented controlled induction motor for high performance application is the capability for easy field weakening and the full utilization of voltage and current rating of the inverter to obtain a wide dynamic speed rangeThis paper describes a Model Reference Adaptive System (MRAS) based scheme using Artificial Neural Network (ANN) for online speed estimation of sensorless vector controlled induction motor drive. The proposed MRAS speed observer uses the current model as an adaptive model. The neural network has been then designed and trained online by employing a back propagation network (BPN) algorithm. The estimator was designed and simulated in Matlab/Simulink. Simulation result shows a good performance of speed estimator. The simulation results show good performance in various operating conditions. Also Performance analysis of speed estimator with the change in resistances of stator is presented. Simulation results show this estimator robust to parameter variations especially resistances of stator

Sensorless control of DC drive using artificial neural network

DOI nefunkčníThe paper deals with the application of an artificial neural network in the speed control of the DC drive without a speed sensor. The sensorless control structure of the DC drive contains the feedforward artificial neural network for speed estimation. The sensorless DC drive was simulated in program Matlab with Simulink toolbox. The main goal was to find the simplest artificial neural network structure with minimum number of neurons, but simultaneously good control characteristics are required. Despite the used neural network, which is very simple, it was achieved satisfactory results. The simulation results were confirmed by measurement of important quantities on a laboratory stand with the DC drive.Web of Science111020

Machine model based Speed Estimation Schemes for Speed Encoderless Induction Motor Drives: A Survey

Speed Estimation without speed sensors is a complex phenomenon and is overly dependent on the machine parameters. It is all the more significant during low speed or near zero speed operation. There are several approaches to speed estimation of an induction motor. Eventually, they can be classified into two types, namely, estimation based on the machine model and estimation based on magnetic saliency and air gap space harmonics. This paper, through a brief literature survey, attempts to give an overview of the fundamentals and the current trends in various machine model based speed estimation techniques which have occupied and continue to occupy a great amount of research space

Jedan novi postupak estimacije brzine vrtnje vektorski upravljanog asinkronog motora zasnovan na adaptivnom sustavu s referentnim modelom i neuronskim mrežama

This paper proposes a new sensorless technique for induction motor drives based on a hybrid MRAS-neural technique, which improves a previously developed neural MRAS based sensorless method. In this paper the open-loop integration in the reference model is performed by an adaptive neural integrator, enhanced here by means of a speed-varying filter transfer function. The adaptive model is based on a more accurate discrete current model based on the modified Euler integration, with a resulting more stable behaviour in the field weakening region. The adaptive model is further trained on-line by a generalized least squares technique, the MCA EXIN + neuron, in which a parameterized learning algorithm is used. As a consequence, the speed estimation presents an improved convergence with higher accuracy and shorter settling time, because of the better transient behaviour of the neuron. A test bench has been set up to verify the methodology experimentally and the results prove its goodness at very low speeds (below 4 rad/s) and in zero-speed operation.U članku se predlaže novi postupak estimacije brzine vrtnje elektromotornog pogona s vektorski upravljanim asinkronim motorom. Postupak se zasniva na hibridnom adaptivnom sustavu s referentnim modelom (MRAS) i neuronskim mrežama. Takav postupak poboljšava prethodno razvijeni estimacijski postupak također zasnovan na »neuronskom MRAS-u«. U radu je realizirana integracija u otvorenoj petlji u referentnom modelu pomoću adaptivnog neuronskog integratora unaprijeđenog s filtrom čija prijenosna funkcija ovisi o brzini motora. Adaptivni je model zasnovan na točnijem diskretnom strujnom modelu motora dobivenom modificiranom Eulerovom integracijom, što rezultira stabilnijim vladanju pogona u režimu slabljenja polja. Adaptivni je model nadalje on-line obučavan korištenjem poopćene metode najmanjih kvadrata (»MCA EXIN+neuron« postupak) pri čemu se koristi parametrirani algoritam učenja. Zbog boljeg ponašanja neurona u dinamičkim stanjima poboljšava se konvergencija estimacije brzine s većom točnošću i manjim vremenom smirivanja. Za eksperimentalnu provjeru predložene metode izgrađena je laboratorijska maketa. Dobiveni rezultati potvrđuju valjanost metode na veoma niskim brzinama (ispod 4 rad/s) i u režimu nulte brzine