• 한국조명전기설비학회:학술대회논문집
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• 한국조명전기설비학회 2007년도 추계학술대회 논문집
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• pp.301-305
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• 2007

This paper is proposed by fuzzy-based MPPT control of photovoltaic to drive induction motor. Design and prototype implement of a fuzzy logic(FL) controller for maxim]m power extraction from a stand-alon photovoltaic Is proposed in this paper. Error and the change of error between maximum power and real power are used by input of fuzzy controller. Moreover, it output changing of voltage from control constant. The validity of this paper is proved by comparing maximum power point tracking and performance of motor drive through comparison fuzzy and PI of tradition method.

• Journal of Electrical Engineering and Technology
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• 제4권4호
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• pp.521-526
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• 2009

In this paper we are interested in the control of Doubly Fed Induction Machine (DFIM) using the Passivity Based Control (PBC). This work presents a solution to the problem of DFIM that requires a state observer. The proposed method shows very important advantages for nonlinear systems, especially in the trajectory tracking to achieve the needed DFIM performance. In the obtained results, the passivity provides high efficiency in DFIM based system, namely in its stability and robustness. An improvement behavior has been observed in comparison to the results given by the RST controller.

• 한국정보전자통신기술학회논문지
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• 제5권1호
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• pp.7-12
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• 2012

In this paper, a direct controller for nonlinear plants using a neural network is presented. The controller is composed of an approximate controller and a neural network auxiliary controller. The approximate controller gives the rough control and the neural network controller gives the complementary signal to further reduce the output tracking error. This method does not put too much restriction on the type of nonlinear plant to be controlled. In this method, a RBF neural network is trained and the system has a stable performance for the inputs it has been trained for. Simulation results show that it is very effective and can realize a satisfactory control of the nonlinear system.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 제36회 하계학술대회 논문집 D
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• pp.2827-2829
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• 2005

In this paper, a direct controller for nonlinear plants using a neural network is presented. The controller is composed of an approximate controller and a neural network auxiliary controller. The approximate controller gives the rough control and the neural network controller gives the complementary signal to further reduce the output tracking error. This method does not put too much restriction on the type of nonlinear plant to be controlled. In this method, a RBF neural network is trained and the system has a stable performance for the inputs it has been trained for. Simulation results show that it is very effective and can realize a satisfactory control of the nonlinear system.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 F
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• pp.2467-2469
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• 1999

In this paper. the stability control system of constant output DC-DC converter is composed of considering a ripple source input and a transient variable sinusoidal power. Also, we design I-PD controller and add phase shift controller. Therefore, it is proposed controller that is stable about the input voltage and load alteration and tracking desired value.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 하계학술대회 논문집 D
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• pp.2498-2500
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• 2004

In this paper, a direct controller for nonlinear plants using a neural network is presented. The controller is composed of an approximate controller and a neural network auxiliary controller. The approximate controller gives the rough control and the neural network controller gives the complementary signal to further reduce the output tracking error. This method does not put too much restriction on the type of nonlinear plant to be controlled. In this method, a RBF neural network is trained and the system has a stable performance for the inputs it has been trained for. Simulation results show that it is very effective and can realize a satisfactory control of the nonlinear system.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1992년도 하계학술대회 논문집 A
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• pp.347-350
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• 1992

In this note, we propose a method for designing a robot controller which can suppress the effects of both the model uncertainty and noisy velocity measurements. The controller is an output feedback compensator of which the constant gains are given in terms of a Riccati equation and a Lyapunov equation. The controller guarantees not only uniform boundedness but uniform ultimate boundedness. The stability result is local but the region can be arbitrarily enlarged at the expense of large control gain. The control law needs neither the exact knowledge of the physical robot parameters nor clean velocity measurements.

• 한국정밀공학회지
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• 제22권7호
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• pp.94-101
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• 2005

This work proposes a new method for describing the hysteresis non-linearity of a piezoelectric actuator. The hysteresis behaviour of piezoelectric actuators, including the minor loop trajectory, are modeled by geometrical relationship between a reference major loop and its minor loops. This hysteresis model is transformed into inverse hysteresis model in order to output compensated voltage with regard to the given input displacement. A feedforward neural network, which is trained by a feedback PID control module, is incorporated to the inverse hysteresis model to compensate unknown dynamics of the piezoelectric system. To show the feasibility of the proposed feedforward-feedback controller, some experiments have been carried out and the tracking performance was compared to that of simple PTD controller.

• 한국정밀공학회지
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• 제16권11호
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• pp.9-15
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• 1999

A piezoelectric actuator is widely used in precision positioning applications due to its excellent positioning resolution. However, the piezoelectric actuator lacks in repeatability because of its inherently high hysteresis characteristic between voltage and displacement. In this paper, a controller is proposed to compensate the hysteresis nonlinearity. The controller is composed of a PID and a neural network part in parallel manner. The output of the PID controller is used to teach the neural network controller by the unsupervised learning method. In addition, the PID controller stabilizes the piezoelectric actuator in the beginning of the learning process, when the neural network controller is not learned. However, after the learning process the piezoelectric actuator is mainly controlled by the neural netwok controller. In this paper, the excellent tracking performance of the proposed controller was verified by experiments and was compared with the classical PID controller.

• 한국지능시스템학회논문지
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• 제20권4호
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• pp.580-585
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• 2010

본 논문은 SOPC 기반 NIOS II 임베디드 프로세서와 C2H를 이용하여 무인 자동 객체 추적 시스템을 구현하였다. 단일PTZ 카메라를 이용한 디지털/아날로그 신호의 입출력, 이미지 프로세싱, 시리얼 통신 그리고 네트워크 통신의 제어를 C2H에 의한 IP 구성과 SOPC 기반 NIOS II 임베디드 프로세서에서 각각의 IP를 효과적으로 제어함으로써 다양한 모니터링 정보를 네트워크로 제공할 수 있는 시스템을 설계, 구현 하였다. SOPC 기반 NIOS II 임베디드 프로세서의 유연성과 고급 알고리듬의 복잡성을 소프트웨어 프로그래밍 언어의 C와 하드웨어 프로그래밍 언어로 유동적으로 컴파일하여 IP화 할 수 있는 특성을 적용함으로서 실시간적으로 무인 객체 추적할 수 있는 시스템의 성능을 향상 시킬 수 있었다.