• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.38.6-38
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• 2001

This paper presents a steady state optimal control algorithm for the weakly coupled discrete time bilinearsystems. The optimal solution for the overall system is obtained by solving a sequence of reduced order algebraic Riccati equations with an arbitrary accuracy. The obtained solutions converge to the optimal solutions by using the iteration method. We verify the proposed method by applying it to a real world numerical example.

• 한국컴퓨터정보학회논문지
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• 제19권2호
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• pp.143-150
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• 2014

본 논문에서는 트리에서의 배달문제를 제안하고, 이를 해결하는 두 개의 알고리즘을 제안한다. 트리상에서의 배달문제는 n개의 서로 다른 이동속도를 갖는 로봇을 이용하여 배달물을 트리의 한 노드에서 다른 노드로 배달하는 시간을 최소화 하는 문제이다. 첫 번째 알고리즘은 핸드오버가 발생하는 장소에 대한 제한을 두어 트리에서의 배달문제를 위한 최적해를 생성한다. 이 알고리즘에서 핸드오버는 주어진 트리의 정점에서만 발생하는 것으로 가정하며, 시작점에서 도착점까지 핸드오버가 발생하는 위치와 핸드오버에 참여하는 로봇의 색인을 순서대로 찾는 방법으로 문제를 해결한다. 두 번째 알고리즘은 첫 번째 알고리즘을 확장하여 핸드오버 장소에 대한 제약을 제거하며, 여전히 최적해를 생성한다. n을 로봇의 수라 하고, m을 트리의 노드 수라 할 때 두 개의 알고리즘은 모두 $O((n+m)^2)$ 시간복잡도를 갖는다.

• 대한전기학회논문지
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• 제43권2호
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• pp.197-205
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• 1994

Security of a power system refers to its robustness relative to a set of imminent disturbances (contingencies) during operation. The socially optimal solution for the actuall level of generation/consumption has been well-known spot pricing at shot-run marginal cost. The main disadvantage of this approach arises because serious contingencies occur quite infrequently. Thus by establishing contractual obligations for contingency offering before an actual operation time through decision feedback we can obtain socially optimal level of system security. Under probabilistic precontract pricing the operating point is established at equal incremental cost of the expected short-run and collapse cost of each participant. Rates for power generation/consumption and for an offer to use during a contingency, as well as information on the probability distribution of contingency need for each participant, are derived so that individual optimization will lead to the socially optimal solution in which system security is optimized and the aggregate benefit is maxmized.

• 해양환경안전학회지
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• 제3권2호
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• pp.93-103
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• 1997

This paper develops a new heuristic, the Excursion Algorithm(EA), for constructing optimal designs for the experiments related with marine environment. The proposed EA consists of three parts: 1) construction of an initial feasible solution, 2) excursions over a bounded region, and 3) stopping rules. It is the second part that distinguishes the EA from the other existing heuristic methods. It turns out that excursions over a bounded feasible and/or infeasible region is effective in alleviating the risks of being trapped at a local optimum. Since this problem is formulated for the first time thesis, other heuristic algorithms do not exist. Therefore, global optimal solutions are obtained by complete enumeration for some cases, and the performance of the EA is evaluated in terms of solution quality. Computational results show that the proposed EA is effective in finding good(or, in many cases, global) solutions to the constrained optimal experimental design problems.

• 전기학회논문지
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• 제57권4호
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• pp.560-566
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• 2008

Today, as the power trades between generation companies and power customer are liberalized, the uncertainty level of operated power system is rapidly increased. Therefore, transmission operators as decision makers for transmission expansion are required to establish a deliberate investment plan for effective operations of transmission facilities considering forecasted conditions of power system. This paper proposes the methodology for the optimal solution of transmission expansion in deregulated power system. The paper obtains the expected value of transmission congestion cost for various scenarios by using occurrence probability. In addition, the paper assumes that increasing rates of loads are the probability distribution and indicates the location of expanded transmission line, the time for transmission expansion with the minimum cost for the future by performing the Montecarlo simulation. To minimize the investment risk as the variance of the congestion cost, Mean-Variance Markowitz portfolio theory is applied to the optimization model by the penalty factor of the variance. By the case study, the optimal solution for transmission expansion plan considering the feature of market participants is obtained.

• 한국추진공학회:학술대회논문집
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• 한국추진공학회 2008년 영문 학술대회
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• pp.293-300
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• 2008

Efficient designing of SRM Grains in the field of Rocketry is still the main test for most of the nations of world for scientific studies, commercial and military applications. There is a strong need to enhance thrust, improve the effectiveness of SRM and reduce mass of motor and burning time so as to allow the general design to increase the weight of payload/on board electronics. Moreover burning time can be increased while keeping the weight of the propellant and thrust in desired range, so as to give the time to control / general design group in active phase for incorporating delayed cut off if required. A mathematical design, optimization & analysis technique for Slotted Tube Grain has been discussed in this paper. In order to avoid the uncertainties that whether the Slotted Tube grain configuration being designed is best suited for achieving the set design goals and optimal of all the available designs or not, an efficient technique for designing SRM Grain and then getting optimal solution is must. The research work proposed herein addresses and emphasizes a design methodology to design and optimize Slotted Tube Grain considering particular test cases for which the design objectives and constraints have been given. In depth study of the optimized solution have been conducted thereby affects of all the independent parametric design variables on optimal solution & design objectives have been examined and analyzed in detail. In doing so, the design objectives and constraints have been set, geometric parameters of slotted tube grain have been identified, performance prediction parameters have been calculated, thereafter preliminary designs completed and finally optimal design reached. A Software has been developed in MATLAB for designing and optimization of Slotted Tube grains.

• 대한산업공학회지
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• 제32권1호
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• pp.9-17
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• 2006

This paper deals with a discrete simulation optimization method for designing a complex probabilistic discrete event simulation. The developed algorithm uses the configuration algorithm that can change decision variables and the stopping algorithm that can end simulation in order to satisfy the given objective value during single run. It tries to estimate an auto-regressive model for evaluating correctly the objective function obtained by a small amount of output data. We apply the proposed algorithm to M/M/s model, (s, S) inventory model, and known-function problem. The proposed algorithm can't always guarantee the optimal solution but the method gives an approximate feasible solution in a relatively short time period. We, therefore, show the proposed algorithm can be used as an initial feasible solution of existing optimization methods that need multiple simulation run to search an optimal solution.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.44-47
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• 1996

Bang-bang control law provides the optimal solution for a minimum-time control problem, but ignores the intermediate path except for the initial and final points. In this paper, a near minimum-time suboptimal fuzzy logic controller is introduced that can control the intermediate path. A dynamic model for a system is established using the average dynamics method of linearization. System model is continuously updated over the control time periods. This makes it suitable for high speed or variable payload applications. Bang-bang control theory is modified and used to derive the preliminary control law. A fuzzy logic algorithm is then applied to adjust and find the best solution. The solution will provide the suboptimal minimum-time control law which can avoid obstacles in the workspace.

• 제어로봇시스템학회지
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• 제1권1호
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• pp.16-16
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• 1995

Like the usual systems, the industrial robot manipulator has some constraints for motion. Usually we hope that the manipulators move fast to accomplish the given task. The problem can be formulated as the time-optimal control problem under the constraints such as the limits of velocity, acceleration and jerk. But it is very difficult to obtain the exact solution of the time-optimal control problem. This paper solves this problem in two steps. In the first step, we find the minimum time trajectories by optimizing cubic polynomial joint trajectories under the physical constraints using the modified evolution strategy. In the second step, the controller is optimized for robot manipulator to track precisely the optimized trajectory found in the previous step. Experimental results for SCARA type manipulator show that the proposed method is very useful.

• Transactions on Control, Automation and Systems Engineering
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• 제1권1호
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• pp.16-20
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• 1999

Like the usual systems, the industrial robot manipulator has some constraints for motion. Usually we hope that the manipulators move fast to accomplish the given task. The problem can be formulated as the time-optimal control problem under the constraints such as the limits of velocity, acceleration and jerk. But it is very difficult to obtain the exact solution of the time-optimal control problem. This paper solves this problem in two steps. In the first step, we find the minimum time trajectories by optimizing cubic polynomial joint trajectories under the physical constraints using the modified evolution strategy. In the second step, the controller is optimized for robot manipulator to track precisely the optimized trajectory found in the previous step. Experimental results for SCARA type manipulator show that the proposed method is very useful.