• Journal of IKEEE
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• v.1 no.1 s.1
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• pp.55-63
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• 1997

The purpose of this paper is to realize robust vector control of an induction motor without speed sensor. In order to do it, the speed of an induction motor is estimated using model reference adaptive system(MRAS) and two rotor flux observers which have robustness to the parameter variation are employed as the reference model and the adjustable model in MRAS speed estimator. The MRAS-based overall control scheme has been implemented on 2.2kW induction motor control system and it is verified that the proposed speed sensorless control scheme is more stable and robust than the conventional schemes.

• Journal of the Institute of Convergence Signal Processing
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• v.8 no.2
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• pp.119-123
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• 2007

This paper investigates a speed sensorless control of induction motor. The control strategy is based on MRAS(Model Reference Adaptive System) using load-torque observer as a reference model for flux estimation. The speed response of conventional MRAS controller characteristics is affected by variations of load torque disturbance. In the proposed system, the speed control characteristics using a load-torque observer control isn't affected by a load torque disturbance. Control algorithm that propose whole system through MATLAB SIMULINK because do modelling simulation result are presented to prove the effectiveness of the adaptive sliding mode controller for the drive variable load of induction motor. Therefore we hope to be extended in industrial application.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.2
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• pp.697-702
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• 2016

To control permanent magnet synchronous motors smoothly, it is important to know the exact parameter values of the stator resistance, various inductances, and the flux linkage of the permanent magnet. In practice, these parameters vary due to a variable operating point, temperature change, or a fault. This paper proposes a MRAS (Model Reference Adaptive System) based parameter estimator and adaptive control scheme. Owing to the non-linearity of the system equation with respect to these parameters, although many schemes proposed previously assumed that some parameters are known, all the parameters were assumed to be unknown. The simulation results revealed the effectiveness of the proposed algorithm.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2064-2066
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• 2002

This paper proposes Model Reference Adaptive Fuzzy System(MRAFS) using Fuzzy Logic Controller(FLC) as a adaptive laws in Model Reference Adaptive System(MRAS) in order to realize the speed-sensorless control of an induction motor. MRAFS estimates the speed of an induction motor with a rotor flux of a reference model and adjustable model in MRAS. Fuzzy logic controller reduces the error of the rotor flux between the reference model and the adjustable model using the error and the change of error as the input of FLC. The computer simulation is executed to verify the propriety and the effectiveness of the proposed system.

• Journal of Advanced Marine Engineering and Technology
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• v.24 no.6
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• pp.102-109
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• 2000

In this industrial induction motor speed and torque controlled drive system, the closed loop control usually requires the measurement of speed or position of amotor. However a sensorless drive of an induction motor has several advantages ; low cost and mechanical simplicity. Thus this paper investigates a field oriented control method without speed and flux sensors. The proposed control strategy is based on the Model Reference Adaptive System(MRAS) using a new flux estimator which replaces integrators with two lag circuits as the reference model. This algorithm may overcome several shortages of conventional MRAS such as integrator problems, small EMF at low speed. The simulation and experimental results indicate good speed responses.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2001.11a
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• pp.181-185
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• 2001

This paper presents a digitally speed sensorless control system for induction motor with direct torque control (DTC). The drive is based on Mode1 Reference Adaptive System (MRAS) using state observer as a reference model fat flux estimation. The system are closed loop stator flux and torque observer for wide speed range that inputs are currents and voltages sensing of motor terminal, model reference adaptive control (MRAS) with rotor flux linkages for the speed turning aignal at low speed range, two hysteresis controllers. The Proposed system is verified through simulation.

• Journal of Electrical Engineering and Technology
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• v.9 no.3
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• pp.873-881
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• 2014

Speed sensorless modes of operation are becoming standard solution in the area of electric drives. This paper presents flux estimator and speed estimator for the speed sensorless vector control of induction motors. The proposed sensorless methods are based on the model reference adaptive system (MRAS) observer and adaptive speed observer (ASO). The proposed speed estimation algorithm can be employed in the power control of grid connected induction generator for wind power applications. Two proposed schemes are verified through computer simulation PSIM and compared their simulation results.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.48 no.3
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• pp.124-130
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• 1999

Speed and position sensors require the additional mounting space, reduce the reliability in harsh environments and increase the cost of motor. Various control algorithms have been proposed for the elimination of speed senor. This paper investigates a novel speed sensorless control of induction motor. The proposed control strategy is based on MRAS(Model Reference Adaptive System) using state observer as a reference model for flux estimation. This algorithm may overcome several shortages of conventional MRAS: integrator problems, small EMF at low speed and relatively large sensitivity to resistance variation. The proposed algorithm is verified through simulation and experiment.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.1
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• pp.183-187
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• 2007

This paper proposes Fuzzy Speed Estimator using Fuzzy Logic Controller(FLC) as a adaptive law in Model Reference Adaptive System(MRAS) in order to realize the speed-sensorless control of an induction motor. Fuzzy Speed Estimator estimates the speed of an induction motor with a rotor flux of the reference model and the adjustable model in MRAS. Fuzzy logic controller reduces the error of the rotor flux between the reference model and the adjustable model using the error and the change of error of the rotor flux as the input of FLC. The experiment is executed to verify the propriety and the effectiveness of the proposed speed estimator.

• Journal of Power Electronics
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• v.15 no.5
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• pp.1274-1285
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• 2015

This paper deals with model reference adaptive system (MRAS) speed estimators based on a secondary flux for linear induction motors (LIMs). The operation of these estimators significantly depends on an adaptation mechanism. Fixed-gain PI controller is the most common adaptation mechanism that may fail to estimate the speed correctly in different conditions, such as variation in machine parameters and noisy environment. Two adaptation mechanisms are proposed to improve LIM drive system performance, particularly at very low speed. The first adaptation mechanism is based on fuzzy theory, and the second is obtained from an LIM mechanical model. Compared with a conventional PI controller, the proposed adaptation mechanisms have low sensitivity to both variations of machine parameters and noise. The optimum parameters of adaptation mechanisms are tuned using an offline method through chaotic optimization algorithm (COA) because no design criterion is given to provide these values. The efficiency of MRAS speed estimator is validated by both numerical simulation and real-time hardware-in-the-loop (HIL) implementations. Results indicate that the proposed adaptation mechanisms improve performance of MRAS speed estimator.