• 한국가축번식학회지
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• 제23권4호
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• pp.371-380
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• 1999

Animal reproductive performance could be affected by several regulatory factors, including nutritional, environmental, and genetic factors. Particularly, during the last half of this century, animal reproductive performance has been remarkably successful in improving the efficiency of livestock production. For some traits efficiency gains have been achieved with little or no knowledge of the genes underlying the traits. And, they have depended upon the phenotypic selection by statistical methods to estimate the genetic parameters of some reproductive traits. In spite of these successes, it is clear that recent advances in both developmental biology and molecular biology are set to revolutionize he practice of animal reproductive performance n the 21th century. (omitted)

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 추계학술대회 논문집 학회본부 B
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• pp.530-532
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• 1999

In adaptive fuzzy control, system designer develops an adaptive law for the output of the unknown plant to track a given signal. The adaptation gains of the adaptive law are critical elements in the overall system, however, they were used to be selected by the designer's experience or intuition. In this paper, genetic algorithm is used to search an optimal adaptation gain and simulation results will be presented to show the improved tracking responses.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1997년도 춘계학술대회 논문집
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• pp.112-117
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• 1997

This paper presents a GA(Genetic Algorithms)-Optical control strategy for the control of the swing motion and the transverse position of the overhead crane. The overhead crane system is defined uncertain due to unknown system parameters such as payload and trolly mass. To control the overhead crane. the GA-Optimal control scheme is suggested. which transfers a trolly to a desired place as fast as possible and minimizes the swing of the payload during the transfer. The genetic algorithms are applied to fine digital optimal feedback gains. A computer simulation demonstrate the performance of the proposed the GA-digital optimal controller for the overhead crane.

• 제어로봇시스템학회논문지
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• 제5권6호
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• pp.656-666
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• 1999

In this paper, we design the optimal PD control system which has the robust performance. This PD control system is designed by applying genetic algorithm (GA) to the determination of proportional gain KP and derivative gain KD that are given by PD servo controller, to make the output of plant follow the output of reference model optimally. These proportional and derivatibe gains are simultaneously optimized in the search domain guaranteeing the robust performance of system. And, this PD control system is compared with $\mu$ -synthesis control system for the robust performance. The PD control system designed by the proposed method has not only the robust performance but also the better command tracking performance than that of the $\mu$ -synthesis control system. The effectiveness of this control system is verified by computer simulation.

• 동력기계공학회지
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• 제18권5호
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• pp.28-34
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• 2014

This paper deals with optimum PI controller design using genetic algorithm to improve control performance and robustness for an oil cooler system. The optimum PI gain was found to minimize an object function, integrated absolute error, and to satisfy control design specifications such as overshoot and settling time based on practical transfer function of the oil cooler system. The control performance and robustness were investigated by comparing indicial responses and Bode diagram analysis with respect to three kinds of PI gains obtained from different gain decision manners. Moreover, the robustness against to input disturbances, sinusoidal wave form and abrupt single pulse, was evaluated. The computer simulation results showed that the suggested optimum gain can establish desirable control performance and strong robustness with easy design process.

• 제어로봇시스템학회논문지
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• 제4권4호
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• pp.458-463
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• 1998

This paper presents an autopilot design method for STT missiles using the intelligent control technique and multiple controllers. The mixed $H_2/H_{\infty}$ control technique is applied for each controller design and the control gains are implemented by using the genetic searching algorithm. To facilitate automatic switching of multiple controllers under different operating conditions, an error based switching scheme is also combined with the multiple controllers at a higher level, which constitutes a hierarchical intelligent control system. It is shown via computer simulation that the proposed autopilot outperforms the conventional one.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.490-490
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• 2000

Parallel genetic algorithms are particularly easy to implement and promise substantial gains in performance. Its basic idea is to keep several subpopulations that are processed by genetic algorithms. Furthermore, a migration mechanism produces a chromosome exchange between subpopulation. In this paper, a new selection method based on non-linear fitness assignment presented. The use of proposed ranking selection permits higher local exploitation search, where the diversity of populations is structure. Experimental results show that the relation between local-global search balance and the probabilities of reaching a desired solution.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.234-234
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• 2000

A direct torque control(DTC) based sensorless speed control system which employs a new closed loop flux observer is proposed. The flux observer takes an adaptive scheduling gains where motet speed is used as the scheduling variable. Adaptive nature comes from the fact that the estimated values of stator resistance and speed are included as observer parameters. The parameters of the PI controllers adopted in the adaptive law for the estimation of stator resistance and motor speed are determined by simple genetic algorithm. Simulation results in low speed region are given for comparison between proposed and conventional flux estimate scheme.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.192-194
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• 1996

This paper propose an optimal scaling gain tuning method of the fuzzy PI controller using Genetic Algorithm(GA). Scaling gains can reflect the control resolution and fuzziness of input/output variables. By the scaling gain method, the design of a fuzzy logic controller(FLC) can be simplified without affecting the system performance in comparison with multi-decision table method. In designing a fuzzy logic controller, the analytic approach method for the optimization is unavailable. Therefore GA is excellent optimization algorithms for scaling gain tuning. Using this optimal scaling gain tuning method, a good performance can be achieved both in transient and steady state.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.495-495
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• 2000

The Stewart platform manipulator is a closed-kinematis chain robot manipulator that is capable of providing high st겨ctural rigidity and positional accuracy. However, this is a complex structure, so controllability of the system is not so good. In this paper, it introduces a new robust motion control algorithm using partial state feedback for a class of nonlinear systems in the presence of modelling uncertainties and external disturbances. The major contribution of this work introduces the development and design of robust observer for the slate and the perturbation w.hich is integrated into a variable structure controller(VSC) structure. The combination of controller/observer gives rise to the robust routine called sliding mode control with sliding perturbation observer(SMCSPO). The optimal gains of SMCSPO are easily obtained by genetic algorithm. Simulation and experiment are presented in order to apply to the stewart platform manipulator. There results show highly' accuracy and performance.