• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 1996.04a
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• pp.125-129
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• 1996

This paper deals with the problem of placement/sizing of distributed piezo actuators to achieve the control objective of vibration suppression. Using the mean square response as a performance index in optimization, we obtain optimal placement and sizing of the actuator. The use of genetic algorithms as a technique for solving optimization problems of placement and sizing is explored. Genetic algorithms are also used for the control strategy. The analysis of the system and response moment equations are carried out by using the Fokker-Planck equation. This paper presents the design and analysis of an active controller and optimal placement/sizing of distributed piezo actuators based on genetic algorithms for a flexible structure under random disturbance, shows numerical example and the result.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.968-971
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• 1996

In this paper, feedback controller is designed for ball-beam system using genetic algorithms. A genetic algorithms are implemented for optimizing gain parameters of feedback controller. We can find optimal point in multi-dimensional search space by using genetic algorithms. Performance of controller is tested by simulation of ball-beam system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.4
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• pp.698-706
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• 2016

In order to move mobile robots to desired locations in a minimum time, optimal control problems have to be solved; however, their analytic solutions are almost impossible to obtain due to robot nonlinear equations. This paper presents a method to get optimal control gains of mobile robots using genetic algorithms. Since the optimal control gains of mobile robots depend on the initial conditions, the initial condition range is discretized to form some grid points, and genetic algorithms are applied to provide the optimal control gains for the corresponding grid points. The optimal control gains for general initial conditions may be obtained by use of neural networks. So the optimal control gains and the corresponding grid points are used to train neural networks. The trained neural networks can supply pseudo-optimal control gains. Finally simulation studies have been conducted to verify the effectiveness of the method presented in this paper.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.04a
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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.

• Proceedings of the SAREK Conference
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• 2008.06a
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• pp.22-29
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• 2008

In order to improve the efficiency of a main transformer in a tilting train, the optimal operation of a cooling system is necessary. For the development of optimal control algorithms of a cooling system, mathematical models of a main transformer cooling system were developed. These include dynamic models of a main transformer, an oil pump, an oil cooler, a blower, and a pipe. Control algorithms for a blower and an oil pump were selected in order to identify the effectiveness of dynamic models. A simulation program was developed by using the developed dynamic models and the selected control algorithms. Simulation results showed good predictions of dynamic behaviors of a main transformer cooling system. Therefore, dynamic models, which were developed in this study, may be effectively used to develop control algorithms of a main transformer cooling system.

• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.446-448
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• 1997

The aim of this paper is to introduce a method designing the optimal model following boiler-turbine control system using genetic algorithms. This boiler-turbine control system is designed by applying genetic algorithms with reference model to the optimal determination of weighting matrices Q, R that are given by LQ regulator problem. These weighting matrices are optimized simultaneously in the search domain selected adequately. The effectiveness of this boiler-turbine control system is verified by computer simulation.

• Journal of the Korea Society of Computer and Information
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• v.1 no.1
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• pp.159-170
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• 1996

In this paper, we propose a generating method for the optimal rules for the nonlinear control system using evolution algorithms and fuzzy controller. With the aid of evolution algorithms optimal rules of fuzzy logic system can be automatic designed without human expert's priori experience and. knowledge. and ran be intelligent control. The approachpresented here generating rules by self-tuning the parameters of membership functions and searchs the optimal control rules based on a fitness value which Is tile defined performance criterion. Computer simulations demonstrates the usefulness of the proposed method In non -linear systems.

• Journal of Industrial Technology
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• v.12
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• pp.61-67
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• 1992

Two control algorithms of active suspension system for improving ride quality are described and their effectiveness is assessed using a quarter car model. Optimal control approach demonstrates great flexibility to meet various running conditions of a vehicle. However, in order to fully utilize the power of optimal control apporach, accurate estimation of the state variables is essential. Simple, yet effective sky-hook algorithm seems to be well suited for real application because of its much relaxed requirements on sensing the stste variables and relative easiness to implment.

• Journal of Korea Society of Industrial Information Systems
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• v.10 no.4
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• pp.54-64
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• 2005

The study investigated genetic algorithms for the optimal control model of maximum height vertical jumping. The model includes forward dynamic simulations by the neural excitation-control variables. Convergence of genetic algorithms is very slow. In this paper the micro genetic algorithm(micro-GA) was used to reduce the computation time. Then a near optimal solution from micro-GA was an initial solution for VF02, which is one of well-developed and proven nonlinear programming algorithms. This approach provided the successful optimal solution for maximum-height jumping without a reasonable initial guess.

• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.587-589
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• 1997

In this paper we design the model following ${\mu}$-synthesis control system for optimal fuel-injection of diesel engine using genetic algorithms. To do this, we give gain and dynamics parameters to the weighting functions and apply genetic algorithms with reference model to the optimal determination of weighting functions that are given by D-K iteration method which can design ${\mu}$-synthesis controller in the state space. These weighting functions are optimized simultaneously in the search domain selected adequately. The effectiveness of this ${\mu}$-synthesis control system for fuel-injection is verified by computer simulation.