• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2001.06a
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• pp.137-140
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• 2001

In this paper, Fuzzzy Digital PI+D Controller plans for load, noise, plant change, Fuzzy Controller makes use of simple four rule and membership function, and plant used three phase Induction Motor. Characteristic of system compared from experimentation respectively the proposed Control System, Digital PID Control and Digital PI+D Control System.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.57 no.2
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• pp.127-134
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• 2008

In this paper, we propose a robust adaptive backstepping control of induction motors with uncertainties using nonlinear disturbance observer(NDO). The proposed NDO is applied to estimate the time-varying lumped uncertainty which are derived from unknown motor parameters and load torque, but NDO error does not converge to zero since the derivate of lumped uncertainty is not zero. Then the fuzzy neural network(FNN) is presented to estimate the NDO error such that the rotor speed to converge to a small neighborhood of the desired trajectory. Rotor flux and inverse time constant are estimated by the sliding mode adaptive flux observer. Simulation results are provided to verify the effectiveness of the proposed approach.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2374-2376
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• 2000

In this paper, we propose an indirect vector control method using Adaptive Neuro-Fuzzy Inference System (ANFIS) parameter estimator. It estimates the rotor time constant when the indirect vector control of induction motor is applied. We use the stator current error that is difference between the current command and estimated current calculated from terminal voltage and current. And two induced current estimate equations are used in training ANFIS.The estimator is trained by the hybrid learning algorithm. Simulation results shows good performance under load disturbance and motor parameter variations.

• Proceedings of the KIPE Conference
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• 2002.11a
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• pp.21-24
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• 2002

The vector control system of an induction motor is the high performance drive system to achieve the instantaneous torque control. The vector control system is greatly divided into the direct control, and the indirect control that the most widely is used, The indirect vector control needs the rotor time constant, which changes widely according to the temperature, frequency, and current amplitude. The incorrect time constant leads to the saturation of magnetic flux or under-excitation phenomena. As a result, that deteriorate the control performance. Therefore, in this paper, the effect of time constant variation is investigated and its on-line tuning algorithm is proposed. The time constant using the torque angles was calculated and that of the validity of algorithm proposed was proved through the computer simulation and the experiment.

• 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 ...

• 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.

• Journal of Power Electronics
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• v.3 no.2
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• pp.115-123
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• 2003

This paper presents analysis, design and simulation for the indirect field orientation control (IFOC) of induction machine drive system. The dynamic performance of the IFOC under nominal and detuned parameters of the induction machine is established. A conventional proportional plus integral-derivative (PI-D) two-degree-of-freedom controller (2DOFC) is designed and analysed for an ideal IFOC induction machine drive at nominal parameters with the desired dynamic response. Varying the induction machine parameters causes a degredation in the dynamic response for disturbance rejection and tracking performance with PI-D 2DOF speed controller. Therefore, conventional controllers can nut meet a wide range of speed tracking performance under parameter variations. To achieve high- dynamic performance, a proposed robust fuzzy logic controllers (RFLC) for d-axis rotor flux, d-q axis stator currents and rotor speed have been designed and analysed. These controllers provide robust tracking and disturbance rejection performance when detuning occurres and improve the dynamic behavior. The proposed REL controllers provide a fast and accurate dynamic response in tracking and disturbance rejection characteristics under parameter variations. Computer simulation results demonstrate the effectiveness of the proposed REL controllers and a robust performance is obtained fur IFOC induction machine drive system.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.48 no.8
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• pp.426-430
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• 1999

In this paper, a speed sensorless algorithm for a high-speed induction motor is proposed. The proposed algorithm simply estimates rotor speed by integrating the deviation between the command current value of a controller and the real current value of the motor. To estimate rotor speed without a speed sensor, a fuzzy speed controller and a neural network speed estimator are applied. Computer simulation and implementation of the proposed system is described.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.25 no.4
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• pp.45-56
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• 2011

This paper is proposed artificial neural network(ANN) rotor resistance estimation of induction motor drive controlled by multi-adaptive fuzzy learning controller(AFLC). A simple double layer feedforward ANN trained by the back-propagation technique is employed in the rotor resistance identification. In this estimator, double models of the state variable estimations are used; one provides the actual induction motor output states and the other gives the ANN model output states. The total error between the desired and actual state variables is then back propagated to adjust the weights of the ANN model, so that the output of this model tracks the actual output. When the training is completed, the weights of the ANN correspond to the parameters in the actual motor. The estimation and control performance of ANN and multi-AFLC is evaluated by analysis for various operating conditions. Also, this paper is proposed the analysis results to verify the effectiveness of this controller.

• Proceedings of the KIPE Conference
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• 2004.07a
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• pp.42-45
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• 2004

This paper is proposed an adaptive fuzzy controller of induction motor drive. The adaptive fuzzy controller approach for an estimate of the rotor time constant which is used to adjust the estimate of the slip angular speed. An estimate of the rotor time constant was obtained using an model reference adaptive system(MRAS) in a fuzzy control scheme. The rotor time constant was estimated by utilizing the rotor nut estimates. This paper is proposed the theoretical analysis as well as the simulation results to verify the effectiveness of the new method.