• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.361-363
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• 2004

시간 및 주파수 특성 분석이 용이한 웨이블릿을 신경회로망에 적용시킨 웨이블릿 신경 회로망의 파라미터 학습 방법에는 오차 역전파 알고리듬 및 유선 알고리듬 등 여러 가지 방법이 있으나 이러한 학습 방법들은 수렴 시간이 오래 걸리는 단점을 가진다. 따라서 본 논문에서는 웨이블릿 신경 회로망의 최적 파라미터를 결정하기 위한 학습 방법으로 일반적으로 비선형 시스템 추정에 주로 사용되는 확장 칼만 필터 알고리듬을 적용한 신경회로망을 제안한다. 또한 제안된 학습 알고리듬을 이용한 웨이블릿 신경 회로망으로 간접 적응 제어기를 설계하여 연속 시간 혼돈 시스템인 Duffing 시스템의 제어에 적용함으로써 확장 칼만 필터 학습 알고리듬을 적용한 웨이블릿 신경 회로망 모델의 우수성을 보인다.

• Journal of Power Electronics
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• v.13 no.6
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• pp.955-963
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• 2013

To acquire a performed and practical solution that is free from chattering, this study proposes the use of an adaptive super-twisting algorithm to drive a single-phase induction motor. Partial feedback linearization is applied before using a super-twisting algorithm to control the speed and stator currents. The load torque is considered an unknown but bounded disturbance. Therefore, a time-varying switching gain that does not require prior knowledge of the disturbance boundary is proposed. A simple sliding surface is formulated as the difference between the real and desired trajectories obtained from the indirect rotor flux oriented control strategy. To illustrate the effectiveness of the proposed control structure, an experimental setup around a digital signal processor (dS1104) is developed and several tests are performed.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.318-322
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• 1996

In this study, a controller design method is proposed for controlling the discrete-time chaotic systems efficiently. Our proposed control method is based on Generalized Predictive Control and uses NARMAX models as a controlled model. In order to evaluate the performance of our proposed controller design method, a proposed controller is applied to Henon system which is a discrete-time chaotic system, and then the control performance of the proposed controller are compared with those of the previous model-based controllers through computer simulations. Through simulations, it is shown that the control performance of the proposed controller is superior to that of the conventional model-based controller.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1996.10a
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• pp.6-10
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• 1996

This paper presents an overview of research results obtained by the authors in a series of publications. Methods are developed both for time-varying and time-invariant for linear and nonlinear. for time domain and frequency domain . and for discrete-time and continuous-time systems. Among the topics presented are: 1. Learning control based on integral control concepts applied in the repetition domain. 2. New algorithms that give improved transient response of the indirect adaptive control ideas. 4. Direct model reference learning control. 5 . Learning control based frequency domain. 6. Use of neural networks in learning control. 7. Decentralized learning controllers. These learning algorithms apply to robot control. The decentralized learning control laws are important in such applications becaused of the usual robot decentralized controller structured.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.468-471
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• 2003

In this paper, a second order sliding mode control that combines with a fuzzy adaptation technique is presented for a nonlinear system with unknown dynamics. The chattering effect that is a representative disadvantage of the sliding mode control is avoided by using the second order sliding mode control instead of the first order sliding mode control. The proposed controller is composed of the equivalent control that is approximated by an online adaptation scheme and the hitting control that is used to constrain the states to maintain on the sub-sliding surface and used to guarantee the system robustness. Simulation results are presented to show the effectiveness of the proposed controller.

• The Transactions of the Korean Institute of Power Electronics
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• v.2 no.1
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• pp.39-45
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• 1997

In this paper, a high speed motor drive system using an indirect adaptive neural network controller is proposed. In the variable high speed motor drives, the speed response can be deteriorated by long settling time and high overshoot. To obtain a good dynamical performance, an adaptive feedforward controller consisted of Neural Network Controller(NNC) and Neural Network Emulator(NNE) is applied. The NNE is used to identify the parameters and characteristics of high speed motor. To train the controller, the weights are dynamically adjusted using the back propagation algorithm. Computer simulation and implementation of the proposed system is described.

• Journal of international Conference on Electrical Machines and Systems
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• v.3 no.2
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• pp.110-115
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• 2014

The reconfigurability of FPGA devices allows designers to evaluate, test and validate a new control algorithm; a new component or prototypes without damaged the real system with the so-called hardware co-simulation. The present paper uses the Xilinx System Generator (XSG) environment to establish and validate a new nonlinear estimator for the rotor time constant inverse that will be exploited to improve the indirect rotor field control of induction motor.

• Proceedings of the KIEE Conference
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• 2003.04a
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• pp.248-252
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• 2003

This paper has a control method proposed for the effective self-tuning fuzzy speed control based on neural network of the induction motor indirect vector control. The vector control of an induction motor provides the decoupled control of the rotor flux magnitude and the torque producing current to performance is desirable. But, the drive performance often degrades for the machine parameter variations and its condition give rise to coupling of flux and torque current. The fuzzy speed control of an induction motor has the robustness about machine parameter variations compared with conventional PID speed control in a way. That proved to be some waf from the true. The purpose of this paper is to improve the adaptation by offering self-turning function to fuzzy speed controller. In this paper, the adaptive mechanism of fuzzy speed control in used ANN(Artificial Neural Network) technique is applied in an IFO induction machine drive, such that the machine can follow a reference model (an ideal field oriented machine) to achieve desired speed. In this paper proved the self-turning method of fuzzy controller has the robustness about parameter variation and the wide range of adaptation by simulation.

• Proceedings of the KIEE Conference
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• 2003.07b
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• pp.1128-1131
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• 2003

This paper proposes a speed control system based on a fuzzy logic approach, integrated with a simple and effective adaptive algorithms. And this paper attempts to provide a thorough comparative insight into the behavior of induction motor drive with PI, direct and improved fuzzy speed controller. A indirect vector controlled induction motor is simulated under varying operating condition. The validity of the comparative results is confirmed by simulation results for induction motor drive system.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.13 no.1
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• pp.60-68
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• 1999

This paper deals with the tracking control problem of industrial robotic manipulators with unknown or changing dynamics. The proposed method makes use of multiple moodels and switching mechanism by fuzzy inference of the manipulator in an indirect adaptive controller architecture. The models used for the indmtification of the manipliator are identical, except for the initial estimates of the unknown inertial pararmeters of the manipulator and its load. The torque input that is applied to the joint actuators is determined at every instant by the identification model that best approximates the robot dynamics. Simulation results are also included to dermnstrate the improvement in the tracking perfermance when the proposed method is used.s used.