• Proceedings of the Korean Operations and Management Science Society Conference
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• 1989.10a
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• pp.45-50
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• 1989

The above theorm states that much larger classes of differential games have an equilibrium. The most severe assumption is the second one. It requires that state dynamic equations be linear on his own control variables. But, the dynamic programming approach applied in the above is hardly implementable for the purpose of computation. It is very difficult to solve (SP$_{it}$) directly. Notice, however, the problem can be transformed into a Hamiltonian maximization problem which is easy to solve if initial conditions are given. In this way, it is possible to design a solution algorithm to problems with nonlinear constraints. The above two theorems probide a basis for such an algorithm.m.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.515-515
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• 2000

The performance of dynamic path following of a wheeled mobile robot with nonholonomic constraints has some drawbacks such as the influence of the initial state. The drawbacks can be overcome by the temporary path generator and modified output. But with the previous input-output linearization method using them, it is difficult to tune the gains, and if there are some modeling errors, the low gain can make the system unstable. And if a high gain is used to overcome the model uncertainties, the control inputs are apt to be large so the system can be unstable. In this paper. an H$_{\infty}$ controller is designed to guarantee robustness to model parameter uncertainties and to consider the magnitude of control inputs. And the solution to Hamilton Jacobi (HJ) inequality, which is essential to H$_{\infty}$ control design, is obtained by nonlinear matrix inequality (NLMI).

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.49 no.2
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• pp.153-160
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• 2013

Control allocation is an important part of a system. It implements the function that map the desired command forces from the controller into the commands of the different actuators. In this paper, the authors present an approach for solving constrained control allocation problem in vessel system by using multi-parametric quadratic programming (mp-QP) algorithm. The goal of mp-QP algorithm applied in this study is to compute a solution to minimize a quadratic performance index subject to linear equality and inequality constraints. The solution can be pre-computed off-line in the explicit form of a piecewise linear (PWL) function of the generalized forces and constrains. The efficiency of mp-QP approach is evaluated through a dynamic positioning simulation for a vessel by using four tugboats with constraints about limited pushing forces and found to work well.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11b
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• pp.274-276
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• 2005

본 논문에서는 단백질 상호작용 네트워크의 복잡성을 제어하고 생물학자가 자신이 설정한 조건을 만족시키는 환경에서 추가적인 다양한 제약 조건을 가하면서 원하는 상호작용 네트워크를 구성하고 조작할 수 있도록 지원하는 Constraints Based Dynamic Protein Interaction Network 이라는 새로운 개념의 단백질 상호작용 네트워크를 소개한다. 본 기법에서는 기존의 단백질 상호작용 네트워크에서 주로 사용하는 단백질 상호작용 정보뿐 아니라 단백질 상호작용에 영향을 미칠 수 있는 개개 단백질의 물리 화학적 특성 및 위치 정보와 상호작용의 환경 정보도 단백질 상호작용 네트워크 구성에 활용한다. 제안된 네트워크상에서 생물학자는 단백질 상호작용 네트워크 구성 조건을 변경하거나 얻어진 네트워크에 변경을 가하면서 점차 자신이 원하는 의미 일은 대사경로 모델을 찾거나, 제약조건의 다양한 조작을 통하여 생물학적 실험을 통하여 얻어진 대사모델의 유효성을 검증하는 것도 가능하다.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.29 no.11 s.242
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• pp.1463-1471
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• 2005

This paper proposes an agent-based scheduling method fur semiconductor wafer fabrication facilities with hard inter-operation temporal constraints. The scheduling problem is to find the feasible schedules that guarantee both logical and temporal correctness. A proposed multi-agent based architecture is composed of scheduling agents, workcell agents, and machine agents. A scheduling agent computes optimal schedules through bidding mechanisms with a subset or entire set of the workcell agents. A dynamic planning-based approach is adopted for the scheduling mechanism so that the dynamic behaviors such as aperiodic job arrivals and reconfiguration can be taken into consideration.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.5
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• pp.497-504
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• 2006

This paper presents a new technique that derives the dynamic acceleration bounds of multiple cooperating robot systems from given individual torque limits of robots. A set of linear algebraic homogeneous equation is derived from the dynamic equations of multiple robots with friction contacts. The mobility of the robot system is analyzed by the decomposition of the null space of the linear algebraic equation. The acceleration bounds of multiple robot systems are obtained from the joint torque constraints of robots by the medium of the decomposed null space. As the joint constraints of the robots are given in the infinite norm sense, the resultant acceleration bounds of the systems are described as polytopes. Several case studies are presented to validate the proposed method in this paper.

• Steel and Composite Structures
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• v.33 no.5
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• pp.747-753
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• 2019

Metaheuristic algorithm is used to solve the weight minimization problem of truss structures considering shape, and sizing design variables. The cross-sectional areas of the line element in trusses are the design variables for size optimization and the changeable joint coordinates are the shape optimization used in this study. The design of plane and spatial truss structures are optimized by metaheuristic technique named Teaching-Learning-Based Optimization (TLBO). Finite element analyses of structures and optimization process are carried out by the computer program visually developed by the authors coded in MATLAB. The four benchmark problems (trusses 2D ten-bar, 3D thirty-seven-bar, 3D seventy-two-bar and 2D two-hundred-bar) taken from literature are optimized and the optimal solution compared the results given by previous studies.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.403-410
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• 1998

An immune system has powerful abilities such as memory recognition and learning how to respond to invading antigens, and has been applied to many engineering algorithms in recent year. In this paper, the combined optimization algorithm (Immune-Genetic Algorithm: IGA) is proposed for multi-optimization problems by introduction the capability of the immune system that controls the proliferation of clones to the genetic algorithm. The new combined algorithm is applied to minimize the total weight of the rotor shaft and the transmitted forces at the bearings in order to demonstrate the merit of the combined algorithm. The inner diameter of the shaft and the bearing stiffness are chosen as the design variables. the results show that the combined algorithm can reduce both the weight of the shaft and the transmitted forces at the bearing with dynamic constraints.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.14 no.7
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• pp.561-568
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• 2004

The nonlinear dynamic characteristic of a straight tube conveying fluid with constraints and an attached mass on the tube is examined in this study An experimental apparatus with an elastomer tube conveying water which has an attached mass and constraints is made and comparisons are made between the theoretical results from the non-linear equation of motion of piping system and the experimental results. The comparisons show that the tube is destabilized as the magnitude of the attached mass increases, and stabilized as the position of the attached mass closes to the fixed end. In case of a small end-mass, the system shows complicated and different types of solutions. For a constant end-mass. the system undergoes a series of bifurcations after the first Hopf bifurcation, as the flow velocity increases. which causes chaotic motions of the tube eventually.

• Journal of Information Processing Systems
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• v.20 no.2
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• pp.226-238
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• 2024

Recently, multi-access edge computing (MEC) has emerged as a promising technology to alleviate the computing burden of vehicular terminals and efficiently facilitate vehicular applications. The vehicle can improve the quality of experience of applications by offloading their tasks to MEC servers. However, channel conditions are time-varying due to channel interference among vehicles, and path loss is time-varying due to the mobility of vehicles. The task arrival of vehicles is also stochastic. Therefore, it is difficult to determine an optimal offloading with resource allocation decision in the dynamic MEC system because offloading is affected by wireless data transmission. In this paper, we study computation offloading with resource allocation in the dynamic MEC system. The objective is to minimize power consumption and maximize throughput while meeting the delay constraints of tasks. Therefore, it allocates resources for local execution and transmission power for offloading. We define the problem as a Markov decision process, and propose an offloading method using deep reinforcement learning named deep deterministic policy gradient. Simulation shows that, compared with existing methods, the proposed method outperforms in terms of throughput and satisfaction of delay constraints.