• 한국조명전기설비학회지:조명전기설비
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• 제13권2호
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• pp.234-234
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• 1999

The aim of this paper is to suggest a design method of the optimal model following control system using genetic algorithm (GA). This control system is designed by applying GA with reference model to the optimal determination of weighting matrices Q, R that are given by LQ regulator problem. The method to do this is that all the diagonal elements of weighting matrices are optimized simultaneously by GA, in the search domain selected adequately. And we design the model following control system to boi1er-turbine equipment by the proposed method. The model following control system designed by this method has the better command tracking performance than that of the control system designed by the trial-and-error method. The effectiveness of this control system is verified by computer simulation.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1993년도 하계학술대회 논문집 A
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• pp.338-340
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• 1993

In this paper, the method for designing a robust linear multivariable model following servo system is proposed. This model following servo system for the (n)th order reference input and the (n)th order disturbance is treated, and is designed so that the (n)th order response of the plant should be kept close to the (n)th order response of the given model by LQ(Linear Quadratic) optimal regulator approach. It is proved that the characteristics of the model following servo system is robust in the presence of the disturbances and the parameter perturbations of the plant dynamics.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1996년도 하계학술대회 논문집 B
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• pp.1069-1072
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• 1996

In modern control engineering, the model following system is a typical method. The model following system's performance is due to algorithm of control law, accuracy of modeling to the plant, and dynamics of the reference model. But the determination of the reference model depends on knowledge of an expert. Using an expert system, the determination method of the reference model was proposed in this paper. So, the reference model can be selected by user who has no Knowledge of dynamics and parameters.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.57-62
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• 2004

This paper deals with a waypoint trajectory following problem for the tilt-rotor UAV under development in Korea (TR-KUAV). In this problem, dynamic model inversion based on the linearized model and Sigma-Phi neural network with adaptive weight update are involved to realize the waypoint following algorithm for the vehicle in the helicopter flight mode (nacelle angle=0 deg). This algorithms consists of two main parts: outer-loop system as a command generator and inner-loop system as stabilizing controller. In this waypoint following problem, the position information in the inertial axis is given to the outer-loop system. From this information, Attitude Command/Attitude Hold logic in the longitudinal channel and Rate Command/Attitude Hold logic in the lateral channel are realized in the inner-loop part of the overall structure of the waypoint following algorithm. The nonlinear simulation based on the TR-KUAV is carried out to evaluate the stability and performance of the algorithm. From the numerical simulation results, the algorithm shows very good tracking performance of passing the waypoints given. Especially, it is observed that ACAH/RCAH logic in the inner-loop has the satisfactory performance due to adaptive neural network in spite of the model error coming from the linear model based inversion.

• 융합신호처리학회논문지
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• 제5권2호
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• pp.138-142
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• 2004

본 논문은 모델추종 제어을 위한 PID제어기법을 제안한다. 이산시간영역에서 제어성능을 개선하기 위해 제안하였고, PID 제어계에 새로운 전치 보상기를 삽입하여 모델추종제어계가 되도록 하였다. 외란이나 부하변동에 의해서 계의 응답이 변할 때 PID 제어기의 이득을 재조정할 필요가 있다. PID 제어계에서 각 PID 이득이 제어계의 성능을 크게 좌우하게 되므로 신경망을 PID제어기에 결합하여 제어계의 성능을 향상시켰고 제안한 제어계에서 PID제어기의 이득은 역전파 알고리즘에 의해 자동적으로 조정되어지도록 하였다. 모델추종 제어계의 제어성능을 확인하기 위하여 제어대상을 직류 서보 전동기의 각 위치로 하였다. 이것을 위치 제어계에 적용하여 실험을 통해 그 성능을 증명하였다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 Proceedings of the Korea Automatic Control Conference, 11th (KACC); Pohang, Korea; 24-26 Oct. 1996
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• pp.372-375
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• 1996

In this paper, a designing method of model following control system for linear descriptor system with disturbances is proposed. The features of this method are:1) both the physical structure of the system and the physical system variables properties can be preserved because there is no necessary to make transformation of this system. 2) boundedness of internal states are proved by means of coprime factorization of descriptor system.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1987년도 한국자동제어학술회의논문집(한일합동학술편); 한국과학기술대학, 충남; 16-17 Oct. 1987
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• pp.876-881
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• 1987

This paper is concerned with a robust model following control scheme for manipulators which contains uncertain terms. Our method consists of nonlinear compensation and linear compensation. The former ensures the robustness of the plant, the later achieves both the desired model following response and the desired initial error convergence.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1986년도 한국자동제어학술회의논문집; 한국과학기술대학, 충남; 17-18 Oct. 1986
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• pp.373-376
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• 1986

This paper proposes a robust model following control system which realizes good properties such as asymptotic stability, disturbance rejection and model following with reduced sensitivity for plant parameter variation. This algorithm can be easily applied to the multivariable control systems and the control structure is simple. As an example the aircraft control system of a convair C-131B is designed and its characteristics are examined by simulation.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1997년도 하계학술대회 논문집 D
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• pp.1165-1167
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• 1997

In this paper, the continuous sliding mode-model following(SM-MF) power system stabilizer(PSS) including closed-loop feedback(CLF) is extended to discrete-time sliding mode-model following(DSM-MF) PSS including CLF.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1997년도 하계학술대회 논문집 D
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• pp.1171-1173
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• 1997

This paper presents the sliding mode observer-model following(SMO-MF) power system stabilizer(PSS) for unmeasurable plant state variables. This SMO-MF PSS can be obtained by combining the sliding mode-model following(SM-MF) including closed-loop feedback(CLF) with the linear foil-order observer(LFOO).