• Proceedings of the KIEE Conference
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• 2000.07b
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• pp.1136-1139
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• 2000

The paper describes a new and rigorous mathematical model for the rotor field oriented system with induction motor which uses the estimated speed and rotor flux based on a Model Reference Adaptive System, as well as the real-time approach. The estimated speed and rotor flux is used for the speed and flux feedback control. The stability and the convergence of the estimator are improved on the basis of hyperstability theory for non-linear systems. The real-time controller and estimator are implemented with a sampling period of $926{\mu}s$ using a dual TMS320C44 floating-point digital signal processor. The validity of the proposed method is verified by simulation, and also, the sensorless control was tested on the propulsion system simulator, used for the development of Korean High-Speed Railway Train (KHSRT) [5].

• Proceedings of the KIEE Conference
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• 2009.04b
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• pp.26-28
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• 2009

This paper proposes maximum torque control of IPMSM drive using adaptive teaming mechanism-fuzzy neural network (ALM-FNN) controller, model reference adaptive fuzzy tonal(MFC) and artificial neural network(ANN). This control method is applicable over the entire speed range which considered the limits of the inverter's current and voltage rated value. For each control mode, a condition that determines the optimal d-axis current $i_d$ for maximum torque operation is derived. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using ALM-FNN, MFC and ANN controller. The proposed control algorithm is applied to IPMSM drive system controlled ALM-FNN, MFC and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper proposes the analysis results to verify the effectiveness of the ALM-FNN, MFC and ANN controller.

• Journal of Power Electronics
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• v.9 no.3
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• pp.343-353
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• 2009

In this paper, a nonlinear controller is proposed for Doubly-Fed Induction Machine (DFIM) drives. The nonlinear controller is designed based on an adaptive backstepping control technique, using a fifth order model of an induction machine in the synchronous d & q axis rotating reference frame, whose d axis coincides with the space voltage vector of the main AC supply, and using the rotor current and stator flux components as state variables. The nonlinear controller can perfectly track the torque reference signal measured in the stator terminals under the condition of unity power factor regulation, in spite of the stator and rotor resistance variations. In order to make the drive system capable of operating in the motoring and generating modes below and above the synchronous speed, two level Space-Vector PWM (SV-PWM) back-to-back voltage source inverters are employed in the rotor circuit. It is confirmed through computer simulation results that the proposed control approach is effective and valid.

• Proceedings of the KIEE Conference
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• 2001.07b
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• pp.986-988
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• 2001

This paper presents a digitally speed sensorless control system for induction motor with direct torque control (DTC). Some drawbacks of the classical DTC are the relatively large torque ripple in a low speed range and notable current pulsation during steady state. They are reflected speed response and increased acoustical noise. In this paper, the DTC quick response are preserved at transient state, while better qualify steady state performance is produced by space vector modulation (SVM). 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 (MRAC) with rotor flux linkages for the speed fuming signal at low speed range, two hysteresis controllers and optimal switching look-up table. Simulation results of the suggest system for the 2.2 [kW] general purposed induction motor are presented and discussed.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.49 no.9
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• pp.626-634
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• 2000

The paper describes a new and rigorous mathematical model using counter-EMF for the rotor field oriented system with induction motor which uses the estimated speed and rotor flux based on a Model Reference Adaptive System as well as the real-time approach. The estimated speed and rotor flux is used for the speed and flux feedback control. The stability and the convergence of the estimator are improved on the basis of hyperstability theory for non-linear systems. The validity of the proposed method is verified by simulation and also the sensorless control was tested on the propulsion system simulator used for the development of Korean High-Speed Railway Train(KHSRT).

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.427-431
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• 1996

The MRAS proposed by Schauder [8] is modified to improve robustness to the change of load torque and/or the variation of the stator resistance. The difference between the voltage and the current model is fed into the current model via proportional and integral gains. In order to generalize the MRAS, supposing that the rotor speed is time varying, we add a compensating term to the current model. It does not alter the Popov's integral inequality condition. Also, the asymptotic stability of the modified MRAS (MMRAS) is shown with the stability proof technique as in the original paper. By the simulation works, it is verified that the MMRAS obtains improved performance than the original MRAS.

• The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.9 no.1
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• pp.54-62
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• 1995

This paper is a study on a speed control of an induction motor used the MRA-DSM(Mode1 Reference Adaptive-Discrete Sliding Mode) controller. In this paper, when controls motor speed, DSM algorithm is proposed for having Robustness against disturbance and parameter variation. and it is also proposed MRA-DSM including the additional load model reference algorithm, which can be compensated the discontinuous control imputs at sliding mode and followed the model Preference independent of parameter variation of control subjects. The control system is composed of the parallel processing control system using the microprocessor for maximizing the performance of control systems and the real time processing. Also it simplifies the hardware composed of controlling the system by software and improves the reliability of the system. And while MRA-DSM control, faster response characteristics of 27.2 % is obtained than DSM control.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.25 no.5
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• pp.22-33
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• 2011

An induction motor operated with a conventional direct self controller(DSC) shows a sluggish response during startup and under changes of torque command. Fuzzy logic controller(FLC) is used in conjection with DSC to minimize these problems. A FLC chooses the switching states based on a set of fuzzy variables. Flux position, error in flux magnitude and error in torque are used as fuzzy state variables. Fuzzy rules are determinated by observing the vector diagram of flux and currents. This paper proposes hybrid fuzzy controller for direct torque control(DTC) of induction motor drives. The speed controller is based on adaptive fuzzy learning controller(AFLC), which provide high dynamics performances both in transient and steady state response. Flux position, error in flux magnitude and error in torque are used as FLC state variables. The speed is estimated with model reference adaptive system(MRAS) based on artificial neural network(ANN) trained on-line by a back-propagation algorithm. This paper is controlled speed using hybrid fuzzy controller(HFC) and estimation of speed using ANN. The performance of the proposed induction motor drive with HFC controller and ANN is verified by analysis results at various operation conditions.

• Proceedings of the KIEE Conference
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• 1991.11a
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• pp.140-143
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• 1991

This paper describes a compensation method for the rotor resistance variation of induction machines in speed sensor-less vector control system using MRAS(model reference adaptive system). In case of rotor resistance variation, the analysis of the conventional speed sensor-less vector control system using MRAS is presented and the compensation method for rotor resistance variation using Fuzzy logic is proposed. In order to confirm the performance of the proposed algorithm, computer simulation is performed.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.714-716
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• 1995

This Paper presents a novel and systematic approach to a self-learning controller. The proposed controller is built on a neural network consisting of a standard back propagation (BNN) and approxinate reasoning (AR). The fuzzy inference and knowledge representation are carried out by the neural network structure and computing, instead of logic inference. An architecture similar to that used by traditional model reference adaptive control system (MRAC) is employed.