• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 ITC-CSCC -3
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• pp.1471-1474
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• 2002

In this paper, we propose a robust indirect adaptive fuzzy state feedback regulator based on Takagi-Sugeno fuzzy model. The proposed adaptive fuzzy regulator is less sensitive to singularity than the conventional one based on the feedback linearization method. Furthermore, the proposed control method is applicable to not only plants with a perfect model but also plants with an imperfect model, which causes uncertainties. We verify the global stability of the proposed method by using Lyapunov method. In order to support the achievement, the application of the proposed adaptive fuzzy regulator to the control of a nonlinear system under the external disturbance is presented and the performance was verified by some simulation result.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2005년도 춘계학술대회 학술발표 논문집 제15권 제1호
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• pp.149-152
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• 2005

In this paper, a robust $H\infty$ stabilization problem to a uncertain discrete-time fuzzy systems with time-varying delay via static output feedback is investigated. The Takagi -Sugeno (T-S) fuzzy model is employed to represent an uncertain nonlinear systems with time-varying delayed state. Using a single Lyapunov function, the globally asymptotic stability and disturbance attenuation of the closed-loop fuzzy control system are discussed. Sufficient conditions for the existence of robust $H\infty$ controllers are given in terms of linear matrix inequalities.

• 드라이브 ㆍ 컨트롤
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• 제9권3호
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• pp.16-22
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• 2012

Nowadays, electro hydrostatic actuator (EHA) has shown great advantages over the conventional hydraulic actuators with valve control system. This paper presents a position control for an EHA using a modified back stepping controller. The controller is designed by combining a backstepping technique and adaptation laws via special Lyapunov functions. The control signal consists of an adaptive control signal to compensate for the nonlinearities and a simple robust structure to deal with a bounded disturbance. Experiments are carried out to investigate the effectiveness of the proposed controller.

• 한국군사과학기술학회지
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• 제3권2호
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• pp.71-80
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• 2000

In Underwater Flight Vehicle depth control system, the followings must be required. Firstly, It need robust depth control performance which can get over parameter variation, modeling error and disturbance. Secondly, It need no oveshoot phenomenon to avoid colliding with ground surface and obstables. Thirdly, It need continuous control input to reduce the acoustic noise and propulsion energy consumption. Finally, It need effective interpolation method which can reduce the dependency of control parameters on speed. To solve these problems, we propose the Fuzzy-PID depth controller with the control parameter interpolators. Simulation results show the proposed control scheme has robust and accurate performance with continuous control input.

• 한국항만학회지
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• 제13권2호
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• pp.381-388
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• 1999

To improve the productivity in the harbor, successful development of an UCT(Unmanned Container Transporter) is needed. Well-designed steering and velocity control systems are the key factor for the development of the UCT. In this paper, a research concerning the achievement of the steering control is introduced. To get an information on the guide line that the UCT should track, the vision system is applied. By using neural network, proper steering angle is gotten fast with less influence of the image disturbance. A simulation based on the UCT kinematics is performed with the measured steering angle, and it shows satisfactory results.

• 전력전자학회논문지
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• 제6권1호
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• pp.90-97
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• 2001

본 논문에서는 시스템의 광성 추정에 의한 가속도 전향보상 방법을 이용한 새로운 속도제어 기법을 제안한다. 제안된 가속도 전향보상 방법에 의해 속도제어기의 대역폭을 충분히 크게 할 수 없는 백터제어 유도전동기 구동시스템에서 속도제어 성능을 향상시킬 수 있고, 외란 토크에 대한 속도회복특성도 개선될 수 있다. 3.7kW 유도전동기 구동시스템에 대한 시뮬레이션과 실험을 통해 제안된 기법의 타당성을 확인하였다.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2005년도 전력전자학술대회 논문집
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• pp.487-489
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• 2005

This paper presents a wavelet-based neural network technology for the detection and classification of the short durations type of power quality disturbances. Transients happen during very short durations to the nano- and microsecond. Thus, a method for detecting and classifying transient signals at the same time and In an automatic combines the properties of the wavelet transform and the advantages of neural networks. Especially, the additional feature extraction to improve the recognition rate is considered. The configuration of the hardware of TMS320C6711 DSP based with 16 channel 20Mhz sampling rate A/D(Analog to Digital) converter and some case studies are described.

• 대한전기학회논문지:시스템및제어부문D
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• 제49권5호
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• pp.225-234
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• 2000

In this paper, we present an nonlinear PID controller with network based compensator which consists of a conventional PID controller that controls the linear components and neuro-compensator that controls the output errors and nonlinear components. This controller is based on the Harris's concept where he explained that the adaptive controller consists of the PID control term and the disturbance compensating term. The resulting controller's architecture is also found to be very similar to that of Wang's controller. This controller adds a self-tuning ability to the existing PID controller without replacing it by compensating the output errors through the neuro-compensator. Various simulations and comparative studies have proven that the proposed nonlinear PID controller produces superior results to other existing PID controllers. When applied to an actual magnetic levitation system which is known to be very nonlinear, it has also produced an excellent results.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 하계학술대회 논문집 C
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• pp.1203-1205
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• 1998

Fault Detection of power system must be rapid and precise over input signal without relation to any disturbance. But, it is difficult to detect current unbalance, over voltage, and underfrequency for digital relay comparison of fault perfectly. In this paper, we measure each phase current and infer type of fault using fuzzy pattern classification.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 D
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• pp.2455-2457
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• 2000

In this paper, it is proposed a neural adaptve control algorithm for boiler-turbine system. Control inputs are constructed using RBF Neural networks and variable structure inputs are added to improve the robustness. This proposed algorithm does not need the information about parameters and can assure the robustness under the output disturbance and parameter perturbations. The results of computer simulations is presented to verify the efficiency of the proposed algorithm.