• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 하계학술대회 논문집 A
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• pp.149-151
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• 2001

This paper proposes the design of STATCOM(static synchronous compensator) stabilizer for improving power system stability using fuzzy logic controller(FLC). The STATCOM DC voltage regulator contributes negative damping to the power system as the installation of STATCOM DC voltage regulator. STATCOM stabiliser is superimposed on the AC voltage regulator to compensate the negative damping effect. To evaluate usefulness of the proposed method, we perform the nonlinear simulation on a single-machine infinite bus system. As results of the simulations, the proposed method shows better control performance than PI controller in terms of damping effects.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1994년도 Proceedings of the Korea Automatic Control Conference, 9th (KACC) ; Taejeon, Korea; 17-20 Oct. 1994
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• pp.714-719
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• 1994

This paper proposes a method of suboptimal control for DC servomotor using a neural network. First we consider a nonlinear observer which is constructed by using an approximated linear dynamics of the nonlinear system and a, neural network. The reccurent neural network is used for the learning of the dynamical system. Next we consider the nonlinear observer. Then, we apply the observer output to nonlinear optimal regulator and confirm the effectiveness by applying the method to the inverse pendulum system.

• 한국정보통신학회논문지
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• 제12권12호
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• pp.2365-2370
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• 2008

본 논문에서는 비선형 시스템의 위치 제어를 위하여 웨이블렛 신경회로망 제어기를 구성하였으며, 웨이블렛 신경회로망은 LQR 제어기의 성능을 향상 시킬 목적으로 사용한다. 불안전한 비선형 시스템을 선형화 시키고 안정화된 선형 시스템을 만들기 위하여 LQR를 사용하며, 외란에 효과적으로 적응하기 위하여 웨이블렛 신경회로망 제어기를 사용한다. 이 제어기를 비선형 시스템의 위치 제어에 적용하여 실험을 통해 그 유효성을 검정하였다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1080-1085
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• 2005

A hybrid control scheme is proposed for the stabilization of backward movement along simple paths for a vehicle composed of a truck and six trailers. The hybrid comprises the combination of a linear quadratic regulator (LQR) and a neurocontroller (NC) that is trained by a genetic algorithm (GA). Acting singly, either the NC or the LQR are unable to perform satisfactorily over the entire range of the operation required, but the proposed hybrid is shown to be capable of providing good overall system performance. The evaluation function of the NC in the hybrid design has been modified from the conventional type to incorporate both the squared errors and the running steps errors. The reverse movement of the trailer-truck system can be modeled as an unstable nonlinear system, with the control problem focusing on the steering angle. Achieving good backward movement is difficult because of the restraints of physical angular limitations. Due to these constraints the system is impossible to globally stabilize with standard smooth control techniques, since some initial states necessarily lead to jack-knife locks. This paper demonstrates that a hybrid of neural networks and LQR can be used effectively for the control of nonlinear dynamical systems. Results from simulated trials are reported.

• 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.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제18권3호
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• pp.269-277
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• 2014

A numerical scheme is proposed to control the BBMB (Benjamin-Bona-Mahony-Burgers) equation, and the scheme consists of three steps. Firstly, BBMB equation is converted to a finite set of nonlinear ordinary differential equations by the quadratic B-spline finite element method in spatial. Secondly, the controller is designed based on the linear quadratic regulator (LQR) theory; Finally, the system of the closed loop compensator obtained on the basis of the previous two steps is solved by the backward Euler method. The controlled numerical solutions are obtained for various values of parameters and different initial conditions. Numerical simulations show that the scheme is efficient and feasible.

• International Journal of Control, Automation, and Systems
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• 제3권spc2호
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• pp.302-307
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• 2005

A novel neuro controller based simple neuro-structure with modified error function is introduced in this paper. This controller consists of two independent controllers, known as the voltage regulator and the angular controller. The voltage regulator is used to modify terminal voltage for the purpose of tracking a reference voltage. The angular controller is utilized to guarantee the stability of the system. In this structure each neuron uses a linear hard limit activation function that depends on the controlled variable and its derivatives. There is no need for parameter identification or any off-line training data. Two proposed controllers are merged by a smooth switch to build a complete controller. The effectiveness of the proposed novel control action is demonstrated through some computer simulations on a Single-Machine Infinite-Bus (SMIB) power system.

• Transactions on Control, Automation and Systems Engineering
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• 제4권4호
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• pp.356-362
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• 2002

In this paper, well-known Takagi-Sugeno fuzzy model is used as the nonlinear plant model and uncertainty is assumed to be included in the model structure with known bounds. Based on the fuzzy models, a numerical robust stability analysis for the fuzzy feedback linearization regulator is presented using Linear Matrix Inequalities (LMI) Theory. For these structured uncertainty, the closed system can be cast into Lur'e system by simple transformation. From the LMI stability condition for Lur'e system, we can derive the robust stability condition for the fuzzy feedback linearization regulator based on Takagi-Sugeno fuzzy model. The effectiveness of the proposed analysis is illustrated by a simple example.

• 전력전자학회논문지
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• 제11권6호
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• pp.535-542
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• 2006

저궤도 인공위성 전력계 시스템의 설계 및 해석을 위한 태양전지 전력조절기의 대신호 안정도해석을 수행한다. 태양전지 전력조절기에서 제어가능한 모든 방법에 따른 태양전지에서 바라본 태양전지 전력조절기의 부하특성을 분류하고, 상태공간해석을 이용하여 태양전지 시스템의 대신호적 안정도를 해석한다. 또한, 본 논문에서는 태양전지 전력조절기의 부하특성을 정전력부하에서 정저항부하로 변환하여 대신호적인 안정도를 확보하는 비선형변환을 제안한다. 제안된 변환기법을 통해 최대전력점 추적제어나 배터리 충전제어 및 전류분배제어가 가능한 병렬 모듈 태양전지 레귤레이터에 적합한 단일 전류 제어기를 구성한다. 제안된 대신호 해석과 저항제어를 검증하기위해, 200W급 태양전지와 100W급 태양전지 전력조절기 두 모듈을 병렬로 구성하여 실험하였다.