• 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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• 2002.07b
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• pp.1165-1168
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• 2002

This paper presents a neural network based self-tuning fuzzy PID control scheme with variable learning rate for sensorless vector controlled induction motor drives. MRAS(Model Reference Adaptive System) is used for rotor speed estimation. When induction motor is continuously used long time. its electrical and mechanical parameters will change, which degrade the performance of PID controller considerably. This paper re-analyzes the fuzzy controller as conventional PID controller structure, introduces a single neuron with a back-propagation learning algorithm to tune the control parameters, and proposes a variable learning rate to improve the control performance. The proposed scheme is simple in structure and computational burden is small. The simulation using Matlab/Simulink and the experiment using DS1102 board show the robustness of the proposed controller to parameter variations.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.23 no.8
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• pp.33-40
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• 2009

This paper presents the high performance control of interior permanent magnet synchronous motor(IPMSM) using space vector(SV) PWM method based on hybrid artificial intelligent(HAI) controller. The HAI controller combines the advantages between adaptive fuzzy control and neural network The SV PWM method is applied to a speed control system of motor in the industry field until now and is feasible to improve harmonic rate of output current, switching frequency and response characteristics. This HAI controller is used instead of conventional PI controller in order to solve problems happening when calculating a reference voltage. The HAI controller improves speed performance by hybrid combination of reference model-based adaptive mechanism method, fuzzy control and neural network. This paper analyzes response characteristics of parameter variation, steady-state and transient-state using proposed HAI controller and this controller compares with conventional fuzzy neural network(FNN) and PI controller. Also, this paper proves validity of HAI controller.

• 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.

• Proceedings of the KIPE Conference
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• 2001.10a
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• pp.443-447
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• 2001

In this paper, a robust sensorless vector control system for induction motors with a speed estimator and an uncertainty observer is presented. At first, the proposed speed estimator is based on the MRAS(Mode Reference Adaptive System) scheme and constructed with a simple fuzzy logic(FL) approach. The structure of the proposed FL estimator is very simple. The input of the FL is the rotor flux error difference between reference and adjustable model, and the output is the estimated incremental rotor speed Secondly, the unmodeled uncertainties such as parametric uncertainties and external load disturbances are modeled by a radial basis function network(RBFN). In the overal speed control system, the control inputs are composed with a norminal control input and a compensated control input, which are from RBFN observer output and the modeling error of the RBFN, repectively. The compensated control input is derived from Lyapunov unction approach. The simulation results are presented to show the validity of the proposed system.

• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.1013-1016
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• 2000

In this study, the estimate speed of rotor in the induction motor with Model Reference Adaptive control System (MARC) principle and to study that vector control system feedbacks speed estimated to speed control system and its result is as follows; Considering with explanation an influence of speed estimation mechanism depend on error about the second resistance size established, it estimates the deviation of the second resistance establishment and exhibits a compensation method, what is more, it designs a reparation program using the fuzzy algorithm and testifies the result with the computer simulation. And besides, it composes the load torque estimation and estimates the load torque, as the result, feedback-compensating the result of estimation, it improves the efficiency. In consequence, it makes a good result for more powerful vector control system about the outside trouble.

• Journal of Institute of Control, Robotics and Systems
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• v.3 no.2
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• pp.149-155
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• 1997

본 논문에서는 비선형 시변 시스템을 제어할 경우 제어시스템의 안정성을 보장하고 성능을 향상시키기 위한 새로운 적응제어 구조를 전개하였다. 주어진 플랜트가 선형 시불변이라는 가정하에 표준 기준 모델 적응제어기가 적용될 경우 발생되는 출력오차는 플랜트의 비선형 시변특성으로 인하여 점근적으로 0에 수렴되지 않는다. 이때 미지의 출력오차를 점근적으로 0에 수렴시키는 방법으로 퍼지보상기를 사용하였으며 결과적으로 플랜트의 비선형 시변 특성을 보상하는 효과를 얻을 수 있었다. 퍼지 보상기로는 출력오차등의 조건에 따라 이득이 변하는 퍼지 PID 보상기를 도입하여 안정하게 설계되도록 노력하였다. 또한 출력오차를 점근적으로 0에 수렴시키는 것은 표준 기준 모델 적응제어기 내부의 모든 파라미터와 신호가 유한하게 됨을 의미하기 때문에, 제어시스템 전체의 안정도를 보장할 뿐만 아니라 결과적으로 과도응답 성능을 향상시킬 수 있게 되었다. 몇가지 예제를 대상으로 시뮬레이션을 수행하고 그 결과를 분석함으로써 비선형 시변 시스템을 제어할 경우 본 논문에서 전개된 새로운 적응제어 구조의 타당성을 확인하였다.

• The Transactions of the Korean Institute of Power Electronics
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• v.10 no.2
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• pp.155-161
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• 2005

This paper presents time a constant estimation of induction motor using MRAS(model reference adaptive system) fuzzy control. The rotor time constant is enabled from the estimation of rotor flux, which has two methods. One is to estimate it based on the stator current and the other is to integrate motor terminal voltage. If the parameters are correct, these two methods must yield the same results. But, for the case where the rotor time constant is over or under estimated, the two rotor nut estimation have different angles. Furthermore their angular positions are related to the polarity of rotor time constant estimation error. Based on these observation, this paper develops a rotor time constant update algorithm using fuzzy control. This paper shows the theoretical analysis as well as the simulation results to verify the effectiveness of the new method.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.2
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• pp.293-300
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• 2011

A number of techniques have been developed for estimation of speed or position in motor drives. The accuracy of these techniques is affected by the variation of motor parameters such as the stator resistance, stator inductance or torque constant. This paper is proposed a neural network based estimator for torque and stator resistance and adaptive fuzzy learning contrroller(AFLC) for speed control in IPMSM Drives. AFLC is chaged fuzzy rule base by rule base modifier for robust control of IPMSM. The neural weights are initially chosen randomly and a model reference algorithm adjusts those weights to give the optimum estimations. The neural network estimator is able to track the varying parameters quite accurately at different speeds with consistent performance. The neural network parameter estimator has been applied to slot and flux linkage torque ripple minimization of the IPMSM. The validity of the proposed parameter estimator and AFLC is confirmed by comparing to conventional algorithm.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.59 no.3
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• pp.279-287
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• 2010

Interior permanent magnet synchronous motor(IPMSM) adjustable speed drives offer significant advantages over induction motor drives in a wide variety of industrial applications such as high power density, high efficiency, improved dynamic performance and reliability. This paper proposes efficiency optimization control of IPMSM drive using adaptive fuzzy learning controller(AFLC). In order to optimize the efficiency the loss minimization algorithm is developed based on motor model and operating condition. The d-axis armature current is utilized to minimize the losses of the IPMSM in a closed loop vector control environment. The design of the current based on adaptive fuzzy control using model reference and the estimation of the speed based on neural network using ANN controller. The controllable electrical loss which consists of the copper loss and the iron loss can be minimized by the optimal control of the armature current. The minimization of loss is possible to realize efficiency optimization control for the proposed IPMSM. The optimal current can be decided according to the operating speed and the load conditions. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using AFLC. Also, this paper proposes speed control of IPMSM using AFLC1, current control of AFLC2 and AFLC3, and estimation of speed using ANN controller. The proposed control algorithm is applied to IPMSM drive system controlled AFLC, the operating characteristics controlled by efficiency optimization control are examined in detail.