• 반도체디스플레이기술학회지
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• 제10권2호
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• pp.25-30
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• 2011

This paper presents a tracking vibration estimation system of the track-following system using a tracking loop gain adjustment algorithm and a genetic algorithm. The algorithms are introduced to estimate accurately the tracking vibration quantity in spite of the uncertainties of the tracking actuator. An estimated actuator model can be found by applying a genetic algorithm. Accordingly, the tracking vibration quantity can be estimated from the measured tracking error, the tracking controller and the estimated actuator model. The proposed tracking vibration estimation method is applied to the track-following system of an optical recording device and is evaluated through the experimental result.

• 한국공작기계학회논문집
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• 제13권3호
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• pp.126-132
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• 2004

This paper proposed trajectory tracking control based on genetic algorithm. Trajectory tracking control scheme are real coding genetic algorithm(RCGA) and back-propagation algorithm(BPA). Control scheme ability experience proposed simulation. Stable tracking control problem of mobile robots have been studied in recent years. These studies have guaranteed stability of controller, but the performance of transient state has not been guaranteed. In some situations, constant gain controller shows overshoots and oscillations. So we introduce better control scheme using real coding genetic algorithm and neural network. Using RCGA, we can find proper gains in several situations and these gains are generalized by neural network. The generalization power of neural network will give proper gain in untrained situation. Performance of proposed controller will verity numerical simulations and the results show better performance than constant gain controller.

• 한국소음진동공학회논문집
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• 제23권4호
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• pp.356-364
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• 2013

This paper presents a tracking gain-up controller design method to control effectively the vibration of tracking actuator caused by external shocks and remaining velocity after seek control. A pole placement constraint is considered to assure a desired transient response against the vibration of tracking actuator. A loop gain-up constraint is introduced to hold the tracking gain-up loop gain and control bandwidth within allowable bounds. The pole placement constraint is expressed by a matrix inequality and the loop gain-up constraint is considered as an objective function so that genetic algorithm can be applied. Finally, a tracking gain-up controller is obtained by integrating a genetic algorithm with LMI design approach. The proposed tracking gain-up controller design method is applied to the track-following system of a DVD recording device and its effectiveness is evaluated through the experimental results.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.512-515
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• 1997

In this paper the fuzzy-genetic controller for path-tracking of WMRs is proposed. Fuzzy controller is implemented to adaptive adjust the crossover rate and mutation rate, and genetic algorithm is implemented to adaptive adjust the control gain during the optimization. The computer simulation shows that the proposed fuzzy-genetic controller is effective.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2003년도 추계학술대회
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• pp.207-212
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• 2003

This paper proposed trajectory tracking control of Mobile Robot. Trajectory tracking control scheme are Real coding Genetic-Algorithm and Back-propergation Algorithm. Control scheme ability experience proposed simulation.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2004년도 추계학술대회 논문집
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• pp.181-186
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• 2004

This paper proposed trajectory tracking control of Mobile Robot. Trajectory tracking control scheme are Real coding Genetic-Algorithm and Back-propergation Algorithm. Control scheme ability experience proposed simulation.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 제37회 하계학술대회 논문집 B
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• pp.1187-1188
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• 2006

This paper a new method which applies a genetic algorithm for determining which sectionalizing switch to operate in order to solve the distribution system loss minimization re-configuration problem. In addition, the proposed method introduces a ultra efficient MPP tracking in a solar power generation system.

• 한국소음진동공학회논문집
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• 제22권3호
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• pp.275-283
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• 2012

This paper presents a design method of a robust tracking controller satisfying multiple constraints using genetic algorithm. A robust $H_{\infty}$ constraint with loop shaping is used to address disturbance attenuation with error limits and a loop gain constraint is considered so as not to enlarge the tracking loop gain and bandwidth unnecessarily. The robust $H_{\infty}$ constraint is expressed by a matrix inequality and the loop gain constraint is considered as an objective function so that genetic algorithm can be applied. Finally, a robust tracking controller can be obtained by integrating genetic algorithm with LMI approach. The proposed tracking controller design method is applied to the track-following system of an optical DVD recording drive and is evaluated through the experimental results.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2004년도 춘계학술대회 논문집
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• pp.241-246
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• 2004

This paper proposed trajectory tracking control of mobile robot. Trajectory tracking control scheme are real coding genetic-algorithm and back-propergation algorithm. Control scheme ability experience proposed simulation. Stable tracking control problem of mobile robots have been studied in recent years. These studios have guaranteed stability of controller, but the performance of transient state has not been guaranteed. In some situations, constant gain controller shows overshoots and oscillations. So we introduce better control scheme using Real coding Genetic Algorithm(RCGA) and neural network. Using RCGA, we can find proper gains in several situations and these gains are generalized by neural network. The generalization power of neural network will give proper gain in untrained situation. Performance of proposed controller will verify numerical simulations and the results show better performance than constant gain controller.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2002년도 춘계학술대회 논문집
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• pp.561-566
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• 2002

This paper proposed trajectory tracking control of Mobile Robot. Trajectory tracking control scheme are Real coding Genetic-Algorithm and Back-propergation Algorithm. Control scheme ability experience proposed simulation.