• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.399-404
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• 2006

The container crane is one of the most important equipment in container terminal. If its working time in cycle could be reduced then container terminal efficiency and service level can be increased. So there are many efforts to reduce working time of container crane. It means how to design the controller with good performance which has small overshoot and swing motion of container crane. We, in this paper, present a state feedback controller not based on LQ theory but RCGA which means real-coded genetic algorithms. RCGA can search state feedback gains in given objective function. several cases of simulations are carried out in order to prove the control effectiveness of the proposed methods.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2006.06b
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• pp.325-330
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• 2006

Integrating processes are frequently encountered in process industries. Generally they are most commonly associated with level control problems. In this paper, tuning formulae of PID controllers for set-point tracking and load disturbance rejection are presented on integrating processes with time delay. In particular, the controller parameters are determined such that performance criteria(IAE, ISE and ITSE) are minimized. Optimal PID parameter sets are obtained by means of a real-coded genetic algorithm(RCGA) and then tuning rules are addressed using obtained PID parameter sets and another RCGA. The performances of the proposed tuning rules are tested on two processes.

• Journal of Navigation and Port Research
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• v.35 no.3
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• pp.227-233
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• 2011

This paper presents the tracking and stabilization problem of a night vision system for marine surveillance. Both a hardware system and software modules are developed to control azimuth and elevation axes independently with compensation for ship motion. A two degree of freedom(2DOF) PID controller is designed and its parameters are tuned using a real-coded genetic algorithm(RCGA). Simulation demonstrates the effectiveness of the proposed method.

• Structural Engineering and Mechanics
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• v.61 no.2
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• pp.283-293
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• 2017

The main contribution of the present paper is to propose an intelligent fuzzy inference system approach for modeling the debonding strength of masonry elements retrofitted with Fiber Reinforced Polymer (FRP). To achieve this, the hybrid of meta-heuristic optimization methods and adaptive-network-based fuzzy inference system (ANFIS) is implemented. In this study, particle swarm optimization with passive congregation (PSOPC) and real coded genetic algorithm (RCGA) are used to determine the best parameters of ANFIS from which better bond strength models in terms of modeling accuracy can be generated. To evaluate the accuracy of the proposed PSOPC-ANFIS and RCGA-ANFIS approaches, the numerical results are compared based on a database from laboratory testing results of 109 sub-assemblages. The statistical evaluation results demonstrate that PSOPC-ANFIS in comparison with ANFIS-RCGA considerably enhances the accuracy of the ANFIS approach. Furthermore, the comparison between the proposed approaches and other soft computing methods indicate that the approaches can effectively predict the debonding strength and that their modeling results outperform those based on the other methods.

• Journal of Advanced Marine Engineering and Technology
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• v.32 no.6
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• pp.974-980
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• 2008

Generally. most of the physical systems affected by disturbance or incomplete knowledge are complex and highly nonlinear. To control under these circumstances. many researches are ongoing in modern control theory recently. But the researches need apparatuses. which can verify the controller for being not damaged the real plant. In this paper. therefore. a seesaw system is considered control system to analyze and apply the control theory. A seesaw system consists of a moving cart on the rail and seesaw frame made to demonstrate the effectiveness of the control theory. The system has balancing and positioning problems. and the driving force is applied on the DC motor of cart. but not on the pivot. The purpose of control is to maintain an equilibrium of seesaw frame in spite of an allowable disturbance. Computer simulations are given to illustrate the control performance of the proposed scheme.

• Journal of Advanced Marine Engineering and Technology
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• v.39 no.10
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• pp.1031-1036
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• 2015

Proportional-integral-derivative (PID) controllers are widely used in industrial sites. Most tuning methods for PID controllers use an empirical and experimental approach; thus, the experience and intuition of a designer greatly affect the tuning of the controller. The representative methods include the closed-loop tuning method of Ziegler-Nichols (Z-N), the C-C tuning method, and the Internal Model Control tuning method. There has been considerable research on the tuning of PID controllers for single-input single-output systems but very little for multi-input multi-output systems. It is more difficult to design PID controllers for multi-input multi-output systems than for single-input single-output systems because there are interactive control loops that affect each other. This paper presents a tuning method for the PID controller for a two-input two-output system. The proposed method uses a real-coded genetic algorithm (RCGA) as an optimization tool, which optimizes the PID controller parameters for minimizing the given objective function. Three types of objective functions are selected for the RCGA, and each PID controller parameter is determined accordingly. The performance of the proposed method is compared with that of the Z-N method, and the validity of the proposed method is examined.

• Journal of Advanced Marine Engineering and Technology
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• v.29 no.1
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• pp.116-123
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• 2005

The PID controllers have been widely accepted in many industrial systems due to their robust performance in a wide range of operating conditions and their functional simplicity To implement a PID controller, its three parameters must be determined for the given plant. Conventional tuning methods are mainly based on experience and experiment and are lack of systematic procedure Recently. to overcome drawbacks of conventional tuning methods, genetic algorithms have been used, In this paper a real-coded genetic algorithm is employed to search for the optimal parameters of the PID controller for speed control of marine gas turbine engines. Simulation results show the effectiveness of the proposed scheme.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.04a
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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.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.13 no.3
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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.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2007.12a
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• pp.63-64
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• 2007

대양 항행하는 거의 모든 선박에서 항해사를 도와 선박의 회두각 유지 및 변침을 목적으로 사용되는 일반적인 오토파일럿 시스템용 대부분 PID형 제어기가 장착되어 있다. 오토파일럿 시스템의 제어상수는 운항 경제성 관점에서 선박의 추진 에너지 손실량(연료소비량)으로써 평가해야 한다. 본 논문에서는 선박 자동 조타 시스템의 정량적인 평가함수를 바탕으로 추진 에너지 손실량이 최소가 되도록 오토파일럿의 제어상수를 구하고자 한다. 이 때 제약조건, 즉 설계사양을 고려한 RCGA를 이용하여 제어상수를 탐색하고, 제안한 방법을 시뮬레이션을 통해 검증한다.