• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.77-78
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• 2008

This paper presents to control speed of induction motors with uncertainties. We use an adaptive backstepping controller with fuzzy neural networks(FNNs) and model reference adaptive system(MRAS) at Indirect vector control method. The adaptive backstepping controller using FNNs can control speed of induction motors even we have a minimum of information. And this controller can be used to approximate most of uncertainties which are derived from unknown motor parameters, load torque such as disturbances. MRAS estimates to rotor resistance and also can find optimal flux to minimize power losses of Induction motor. Indirect vector PI current controller is used to keep rotor flux constant without measuring or estimating the rotor flux. Simulation and experiment results are verified the effectiveness of this proposed approach.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.2
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• pp.1044-1050
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• 2014

Maximum torque per ampere vector controller is widely used to control permanent magnet synchronous motors. For the controller to work properly, it is important to know the exact values of motor parameters such as a stator resistance, inductances, and the flux linkage of the permanent magnet. In this paper, an adaptive control algorithm is proposed to estimate these parameters using MRAS(Model Reference Adaptive System). Simulation results demonstrate the effectiveness of the proposed algorithm.

• Proceedings of the KIPE Conference
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• 2002.07a
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• pp.188-191
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• 2002

This paper presents a method for on-line estimation of rotor time constant of induction motor. The proposed method applies a model reference adaptive system(MRAS) using rotor flux vector. The MRAS consists of two independent observers to estimate the rotor flux vector; one based on voltage equations of rotor flux vector, the other based on current equations of them. The MRAS utilizes concept of auxiliary variables to normalize observer output and decrease high-frequency components of its input. Experimental results verify the validity and usefulness of proposed method

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.162.5-162
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• 2001

This paper presents a indirect vector control system for induction motors using an adaptive fuzzy logic(AFL) speed estimator. The proposed speed estimator is based on the MRAS(Mode Referece Adaptive System) scheme. In general, the MRAS speed estimation approaches are more simple than any other strategies. However, there are some difficulties in the scheme, which are strong sensitivity to the motor parameters variations and necessity to detune the estimator gains caused by different speed area. In this paper, the AFL speed estimator is proposed to solve the problems. The structure of the proposed AFL is very simple. The input of the AFL is the rotor flux error difference between reference and adjustable model, and the output is the estimated incremental rotor speed. Moreover, the back propagation algorithm is combined to adjust the parameters of the fuzzy logic to the most appropriate values during the operating the system. Finally, the validity of the ...

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.830-831
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• 2011

The paper makes an investigation on a speed and stator flux linkage estimator for permanent magnet synchronous motor (PMSM) sensorless drives using the technology of model reference adaptive system (MRAS). The designed estimator including two models and two adaptive estimating laws is proved to be stable by the Popov hyper-stability theory. The speed, the stator flux linkage and the resistance are estimated accurately by the proposed estimator while overcoming the shortcoming of the traditional one. The experiment results demonstrate its effectiveness.

• Journal of Electrical Engineering and Technology
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• v.8 no.3
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• pp.530-543
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• 2013

This paper describes a control scheme of speed sensorless fuzzy direct torque control (FDTC) of permanent magnet synchronous motor for electric vehicle (EV). Electric vehicle requires fast torque response and high efficiency of the drive. Speed sensorless FDTC In-wheel PMSM drives without mechanical speed sensors at the motor shaft have the attractions of low cost, quick response and high reliability in electric vehicle application. This paper presents a new approach to estimate the speed of in-wheel electrical vehicles based on Model Reference Adaptive System (MRAS). The direct torque control suffers in low speeds due to the effect of changes in stator resistance on the flux measurements. To improve the system performance at low speeds, a PI-fuzzy resistance estimator is proposed to eliminate the error due to changes in stator resistance. High performance sensorless drive of the in-wheel motor based on MRAS with on line stator resistance tuning is established for four motorized wheels electric vehicle and the whole system is simulated by matalb/simulink. The simulation results show the effectiveness of the new control strategy. This proposed control strategy is extensively used in electric vehicle application.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.52 no.11
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• pp.541-547
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• 2003

Speed and torque controls of permanent magnet synchronous motors are usually attained by the application of position and speed sensors. However, speed and position sensors require the additional mounting space, reduce the reliability in harsh environments and increase the cost of a motor. Therefore, many studies have been peformed for the elimination of speed and position sensors. This paper investigates a novel speed sensorless control of a permanent magnet synchronous motor. The proposed control strategy is based on the MRAS(Model Reference Adaptive System) using the state observer model with the current error feedback and the magnet flux model as two models for the back-emf estimation. The proposed algorithm is verified through the simulation and experiment.

• The Transactions of the Korean Institute of Power Electronics
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• v.19 no.1
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• pp.71-79
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• 2014

In this paper, a new grid voltage estimation algorithm without voltage sensors is proposed for the three-phase vienna rectifier. Generally, input voltage sensor circuits increase size and cost of the PWM rectifier In order to reduce the cost and size and in order to increase reliability from the electrical noise, grid voltage estimation scheme without input voltage sensor is highly required. In this paper, the grid voltage estimation algorithm is proposed by a simple MRAS(Model Reference Adaptive System) observer without input voltage sensors. The validity of the proposed method is proven by simulation and experiment on the three-phase vienna rectifier system.

• Journal of Electrical Engineering and Technology
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• v.2 no.2
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• pp.241-248
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• 2007

The accuracy of all the schemes that belong to vector controlled induction machine drives is strongly affected by parameter variations. The aim of this paper is to examine iron losses and magnetic saturation effect in sensorless vector control of induction machines. At first, an approach to induction machine modelling and vector control scheme, which account for both iron loss and saturation, is presented. Then, a model reference adaptive system (MRAS) based speed estimator is developed. The speed estimation is modified in such a way that iron losses and the variation in the saturation level are compensated. Thus by substituting an artificial neural network flux estimator into the MRAS speed estimator. Experimental results are presented to verify the effectiveness of the proposed approach.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.51 no.1
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• pp.18-27
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• 2002

This paper presents an implementation of digital high-performance speed sensorless control system of an induction motor drives with Direct Torque Control(DTC). The system consists of closed loop stator flux and torque observer, speed and torque estimators, two hysteresis controllers, an optimal switching look-up table, IGBT voltage source inverter, and TMS320C31 DSP controller board. The stator flux observer is based on the combined current and voltage model with stator flux feedback adaptive control for wide speed range. The speed estimator is using the model reference adaptive system(MRAS) with rotor flux linkages for speed turning signal estimation. In order to prove the suggested speed sensorless control algorithm, and to obtain a high-dynamic robust adaptive performance, we have some simulations and actual experiments at low(20rpm) and high(1000rpm) speed areas. The developed speed sensorless system are shown a good speed control response characteristic, and high performance features using 2.2[kW] general purposed induction motor.