• The Transactions of the Korean Institute of Power Electronics
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• v.15 no.4
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• pp.282-287
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• 2010

This paper proposes a new nonlinear speed controller with a fuzzy load torque observer based on the Takagi-Sugeno fuzzy method for a permanent magnet synchronous motor(PMSM). The LMI conditions are derived for the existence of the proposed nonlinear speed controller and fuzzy load torque observer, and the LMI parameterization to obtain the gain matrices of the controller and observer is given. In this paper, to verify the performance of the proposed nonlinear speed controller and fuzzy load torque observer, and the simulation and experimental results are demonstrated under motor parameter and load torque variations.

• Proceedings of the KIEE Conference
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• 2003.07b
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• pp.1111-1113
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• 2003

This paper proposes an effective self-turning algorithm based on Artificial Neural Network (ANN) for fuzzy speed control of the indirect vector controlled induction motor. Indirect vector control method divides and controls stator current by the flux and the torque producing current so that the dynamic characteristic of induction motor may be superior. However, if motor parameter changes, the flux current and the torque producing one's coupling happens and deteriorates the dynamic characteristic. The fuzzy speed controller of an induction motor has the robustness over the effect of this parameter variation than a conventional PI speed controller in some degree. This paper improves its adaptability by adding the self-tuning mechanism to the fuzzy controller. For tracking the speed command, its membership functions are adjusted using ANN adaptation mechanism. This adaptability could be embodied by moving the center positions of the membership functions. Proposed self-tuning method has wide adaptability than existent fuzzy controller or PI controller and is proved robust about parameter variation through Matlab/Simulink simulation.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2009.05a
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• pp.416-419
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• 2009

The widely used control theory based design of PI family controllers fails to perform satisfactorily under-parameter variation, nonlinear or load disturbance. In high performance applications, it is useful to automatically extract the complex relation that represent the drive behaviour. The use of loaming through example algorithms can be a powerful tool for automatic modelling variable speed drives. They can automatically extract a functional relationship representative of the drive behavior. These methods present some advantages over the classical ones since they do not rely on the precise knowledge of mathematical models and parameters. The paper proposes high performance speed and current control of synchronous reluctance motor(SynRM) drive using adaptive loaming mechanism-fuzzy neural network (ALM-FNN) and fuzzy logic control(FLC) controller. The proposed controller is developed to ensure accurate speed and current control of SynRM drive under system disturbances and estimation of speed using artificial neural network(ANN) controller. Also, this paper proposes the analysis results to verify the effectiveness of the ALM-FNN and ANN controller.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.11 no.6
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• pp.379-387
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• 1986

The architecture and design of a highperformance general-purpose microprogrammable sequential controller are presented. Conventional sequential controllrs are limited by speed, memory requirement, flexibility and programming ease. The proposed controller solves those limitations. it has a fixed hardware and is microprogrammable throutgh firmware modification. It is especially designed for complex high-speed sequential controllers requiring large I/O capabilities, such as industrial process ocntroller or power electronic conversion controller.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.54 no.8
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• pp.395-401
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• 2005

This paper presents Hybrid PI(HBPI) controller of induction motor drive using fuzzy control. In general, PI controllers used in computer numerically controlled machines process fixed gain. They may perform well under some operating conditions, but not all. To increase the robustness of fixed gain PI controller, HBPI controller proposes a new method based self tuning PI controller. HBPI controller is developed to minimize overshoot and settling time following sudden parameter changes such as speed, load torque, inertia, rotor resistance and self inductance. The results on a speed controller of induction motor are presented to show the effectiveness of the proposed gam tuner. And this controller is better than the fixed gains one in terms of robustness, even under great variations of operating conditions and load disturbance.

• Proceedings of the KIEE Conference
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• 2006.04b
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• pp.176-178
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• 2006

This paper is intended to research the speed response of switched reluctance motor (SRM) using sliding mode control method when load is changing. The simulation compare only control by PI controller with control by PI controller and sliding mode controller. It compare with speed response using MATLAB/SIMULINK.

• Proceedings of the KIPE Conference
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• 1998.11a
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• pp.1-4
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• 1998

In this paper, an auto-tuning method for fuzzy controller based on the neural network is presented. The backpropagated error of neural emulator offers the path which reforms the fuzzy controller's membership functions and fuzzy rule, and used for speed control of switched reluctance motor. The experiments are performed to verify the capability of proposed control method on 6/4 salient type SRM. The results show that fuzzy-neural controller is suitable for wide speed range.

• Proceedings of the KIEE Conference
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• 2006.07b
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• pp.925-926
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• 2006

This paper presents a sensorless speed control of an interior permanent magnet synchronous motor using an adaptive integral binary observer and fuzzy logic controller. In view of composition with a main loop regulator and an auxiliary loop regulator, the binary observer has a property of the chattering alleviation in the constant boundary layer. However, the steady state estimation accuracy and robustness are dependent upon the width of the constant boundary. In order to improve the steady state performance of the binary observer, the binary observer is formed by adding extra integral dynamics to the switching hyperplane equation. Also, because the conventional fixed gain PI controller are very sensitive to step change of command speed, parameter variations and load disturbance, the fuzzy logic controller is used to compensate a fixed gain PI controller. Therefore, a gain PI is fixed and the IPMSM is drived at another speed region. The effectiveness of the proposed the adaptive integral observer and the fuzzy logic controller are confirmed by experimental results.

• Journal of Electrical Engineering and Technology
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• v.13 no.1
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• pp.201-210
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• 2018

This paper proposes a new start-up method for a load commutated inverter (LCI) in a large synchronous gas-turbine generator. The initial rotor position for start-up torque is detected by the proposed initial angle detector, which consists of an integrator and a phase-locked loop. The initial rotor position is accurately detected within 150ms, and the angle difference between the real position and the detected position is less than 1%. The LCI system operates in two modes (forced commutation mode and natural commutation mode) according to operating speed range. The proposed controllers include a forced commutation controller for the low-speed range, a PI speed controller and a PI current controller, where the forced commutation controller is connected to the current controller in parallel. The current controller is modeled by Matlab/Simulink, where a six-pulse delay of the thyristor and a processing delay are considered by using a zero-order hold. The performance of the proposed start-up method is evaluated in Matlab/Psim at standstill and at low speed. To verify the feasibility of the method, a 5kVA LCI system prototype is implemented, and the proposed initial angle detector and the system performance are confirmed by experimental results from standstill to 900rpm.

• Proceedings of the KIEE Conference
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• 2005.07b
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• pp.1453-1455
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• 2005

This paper presents self tuning PI controller of IPMSM drive using neural network. Self tuning PI controller is developed to minimize overshoot, rise time and settling time following sudden parameter changes such as speed, load torque and inertia. Also, this paper is proposed speed control of IPMSM using neural network and estimation of speed using artificial neural network(ANN) controller. The results on a speed controller of IPMSM are presented to show the effectiveness of the proposed gain tuner. And this controller is better than the fixed gains one in terms of robustness, even under great variations of operating conditions and load disturbance.