• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.269-269
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• 2000

This paper presents a predictive control with H$_{\infty}$ suboptimal performance which is robust to disturbances and has a guaranteed stability. In order to derive the control law conveniently, state-space based approach, where the state variable is involved explicitly in the controller design and implementation is allowed. So an input-output model is converted to an equivalent observable canonical state-space form. The suggested control guarantees the norm bounded system output values from disturbances. A systematic way using the LMI method is presented to obtain appropriate parameters for Quadratic stability condition and optimization problem.

• Journal of Institute of Control, Robotics and Systems
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• v.3 no.6
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• pp.582-587
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• 1997

This paper deals with the control problem under nonlinear boiler systems with noise, and input constraints. MCMBPC(Multivariable Constrained Model-Based Predictive Controller) proposed by Wilkinson et al.[10,11] is used and nominal model is modified in this paper in order to applied to nonlinear boiler systems with feed-forward terms. The solution of the cost function optimization constrained on input and/or output variables is achieved using quadratic programming, via singular value decomposition(SVD). The controller designed is shown to satisfy the constraints and to have excellent tracking performance via simulation applied to nonlinear dynamic drum boiler turbine model for 16OMW unit.

• Journal of the Korean Institute of Telematics and Electronics
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• v.22 no.3
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• pp.91-94
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• 1985

A method optimizing selection of a state weighting matrix is presented. The state weight-ing matrix is chosen so that the closed-loop system responses closely match to the ideal model responses. An algorithm is presented which determines a positive semidefinite state weighting matrix in the linear quadratic optimal control design problem and an numerical example is given to show the effect of the present algorithm.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.31 no.7
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• pp.18-24
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• 1982

We derived an optimal control of the Stochastic Bilinear Systems. For that we, firstly, formulated stochastic bilinear system and estimated its state when the system state is not directly observable. Optimal control problem of this system is reviewed on the line of three optimization techniques. An optimal control is derived using Hamilton-Jacobi-Bellman equation via dynamic programming method. It consists of combination of linear and quadratic form in the state. This negative feedback control, also, makes the system stable as far as value function is chosen to be a Lyapunov function. Several other properties of this control are discussed.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.28-33
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• 1994

An optimal controller, e.g. LQG controller, may not be realistic in the sense that the required control power may not be achieved by existing actuators, and the measured output is not satisfactory. To be realistic, the controller should meet such constraints as sensor or actuator limitation, performance limit, etc. In this paper, the lnput/Output Variance Constrained (IOVC) control problem will be considered from the viewpoint of mathematical programming. A dual version shall be developed to solve the IOVC control problem, whose objective is to find a stabilizing control law attaining a minimum value of a quadratic cost function subject to the inequality constraint on each input and output variance for a stabilizable and detectable plant. One approach to the constrained optimization problem is to use the Kuhn-Tucker necessary conditions for the optimality and to seek an optimal point by an iterative algorithm. However, since the algorithm uses only the necessary conditions, the convergent point may not be optimal solution. Our algorithm will guarantee a sufficiency.

• Journal of the Korea Concrete Institute
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• v.11 no.6
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• pp.57-67
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• 1999

Optimization scheme is presented for the design of precast prestressed double-tee beams used as slabs in the parking or market structures. The objective considered is defined by a function that minimizes the hight of the double-tee beam, including the prefabricated element and the concrete topping poured in a second phase. The Sequential Quadratic Programming method is adopted to solve the problem. As an example 12 double-tee beams are designed with the design loads of the current design code of our country. The results from optimization process show that at least 29cm less in overall height than that designed by PCI design handbook. The section determined from the optimization process was refined for practical considerations. A MathCad 7.0 Pro Spreadsheet was prepared to verify all ACI requirements for flexure, shear and deflections. Flexural tests are performed on four full-scale 12.5m prototype models and show that all the specimens are fully comply the flexural strength requirements as specified by ACI 318-95. The present optimization scheme can be used for wider application of the design of precast prestressed double-tee beams with different materials and configurations particularly for in a large scale or for important designs.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.31 no.8
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• pp.663-671
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• 2007

In this work, shape optimization of a wire-wrapped fuel assembly in a liquid metal reactor has been carried out by combining a three-dimensional Reynolds-averaged Navier-Stokes analysis with the radial basis neural network method, a well known surrogate modeling technique for optimization. Sequential Quadratic Programming is used to search the optimal point from the constructed surrogate. Two geometric design variables are selected for the optimization and design space is sampled using Latin Hypercube Sampling. The optimization problem has been defined as a maximization of the objective function, which is as a linear combination of heat transfer and friction loss related terms with a weighing factor. The objective function value is more sensitive to the ratio of the wire spacer diameter to the fuel rod diameter than to the ratio of the wire wrap pitch to the fuel rod diameter. The optimal values of the design variables are obtained by varying the weighting factor.

• Proceedings of the KSME Conference
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• 2004.04a
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• pp.1418-1423
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• 2004

Optimization of a heat exchanger with internally finned circular tubes has been performed for three-dimensional periodically fully developed turbulent flow and heat transfer. The design variables of fin number N, fin width ($d_1,d_2$) and fin height(H), are numerically optimized for the limiting conditions of $N=22{\sim}37$, $d_1=0.5{\sim}1.5$ mm, $d_2=0.5{\sim}1.5$ mm, $H=0.1{\sim}1.5$. Due to the periodic boundary conditions along main flow direction, the three layers of meshes are considered. The CFD and the mathematical optimization are coupled to optimize the heat exchanger. The flow and thermal fields are predicted using the finite volume method and the optimization is carried out by using the sequential quadratic programming (SQP) method which is widely used in the constrained nonlinear optimization problem.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.29 no.4 s.235
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• pp.460-468
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• 2005

Analyses of flow and heat transfer characteristics and shape optimization of internally finned circular tubes have been performed for three-dimensional periodically fully developed turbulent flow and heat transfer. CFD and mathematical optimization are coupled in order to optimize the shape of heat exchanger. The design variables such as fin widths $(d_{1},\;d_{2})$ and fin height (h) are numerically optimized by minimizing the pressure loss and maximizing the heat transfer rate for limiting conditions of $d_{1}=0.2\~1.5\;mm,\;d_{2}=0.2\~1.5\;mm,$ and $h=0.2\~1.5mm$. Due to the periodic boundary conditions along main flow direction, the three layers of meshes are considered. The flow and thermal fields are predicted using the finite volume method and the optimization is carried out by means of the sequential quadratic programming (SQP) method which is widely used in the constrained nonlinear optimization problem.

• Journal of the Society of Naval Architects of Korea
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• v.49 no.1
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• pp.68-78
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• 2012

A layout of an LNG FPSO should be elaborately determined as compared with that of an onshore plant because many topside process systems are installed on the limited area; the deck of the LNG FPSO. Especially, the layout should be made as multi-deck, not single-deck and have a minimum area. In this study, a multi-floor layout for the liquefaction process, the dual mixed refrigerant(DMR) cycle, of LNG FPSO was determined by using the optimization technique. For this, an optimization problem for the multi-floor layout was mathematically formulated. The problem consists of 589 design variables representing the positions of topside process systems, 125 equality constraints and 2,315 inequality constraints representing limitations on the layout of them, and an objective function representing the total layout cost. To solve the problem, a hybrid optimization method that consists of the genetic algorithm(GA) and sequential quadratic programming(SQP) was used in this study. As a result, we can obtain a multi-floor layout for the liquefaction process of the LNG FPSO which satisfies all constraints related to limitations on the layout.