• Journal of the Korean Operations Research and Management Science Society
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• v.32 no.2
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• pp.99-107
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• 2007

We consider (worst-case) robust optimization versions of the Economic Order Quantity (EOQ) model with decreasing cost functions. Two variants of the EOQ model are discussed, in which the purchasing costs are decreasing power functions in either the order quantity or demand rate. We develop the corresponding worst-case robust optimization models of the two variants, where the parameters in the purchasing cost function of each model are uncertain but known to lie in an ellipsoid. For the robust EOQ model with the purchasing cost being a decreasing function of the demand rate, we derive the analytical optimal solution. For the robust EOQ model with the purchasing cost being a decreasing function of the order quantity, we prove that it is a convex optimization problem, and thus lends itself to efficient numerical algorithms.

• Journal of the Korean Statistical Society
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• v.30 no.1
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• pp.163-178
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• 2001

We consider the experimental design problem of selecting values of design variables x for observation of a response y that depends on x and on model parameters $\theta$. The form of the dependence may be quite general, including all linear and nonlinear modeling situations. The goal of the design selection is to efficiently estimate functions of $\theta$. Three new criteria for selecting design points x are presented. The criteria generalized the usual Bayesian optimal design criteria to situations n which the prior distribution for $\theta$ amy be uncertain. We assume that there are several possible prior distributions,. The new criteria are applied to the nonlinear problem of designing to estimate the turning point of a quadratic equation. We give both analytic and computational results illustrating the robustness of the optimal designs based on the new criteria.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.6
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• pp.1179-1185
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• 2010

This paper deals with a robust controller design problem for a small one-link robot arm system subject to input time delay and load variations. The uncertain parameters of the system are considered as a disturbance input. A disturbance observer(DOB) has been designed to alleviate disturbance effects and to compensate performance degradation owing to the time-delay. This paper employs a new DOB structure for non-minimum phase systems together with the Smith predictor. We propose a new controller for reducing the both effects of disturbance and time-delay. In order to test the performance of proposed controller, four different other control laws are compared with the proposed one by computer simulations. The simulation results show the effectiveness of the proposed control method.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.1849_1850
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• 2009

In this paper, we propose an adaptive observer for electrically driven mobile robots in the presence of parametric uncertainties. To estimate the unmeasured velocities of the mobile robot, we develop an adaptive observer using the transformation matrices which can solve the difficulty caused by the quadratic velocity terms depending on the uncertain parameters. Using the Lyapunov stability theory, we prove that all signals in a closed-loop system converge to zero. Finally, the simulation results demonstrate the effectiveness of the proposed observer system.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.1703_1704
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• 2009

Fuzzy neural ship controllers is used in ship steering control. It can make full use of the advantage of all kinds of intelligent algorithms. This provides an efficient way for this paper. An RBF neural network and GA optimization are employed in a fuzzy neural controller to deal with the nonlinearity, time varying and uncertain factors. Utilizing the designed network to substitute the conventional fuzzy inference, the rule base and membership functions can be auto-adjusted by GA optimization. The parameters of neural network can be decreased by using union-rule configuration in the hidden layer of the network. The ship control quality is effectively improved in case of appending additional sea state disturbance. The performance of controller is evaluated by the system simulation using simulink tools.

• International Journal of Precision Engineering and Manufacturing
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• v.3 no.4
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• pp.78-86
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• 2002

In this paper, LMI (Linear Matrix Inequality) based on H$\_$$\infty$/ controller for a lire of sight (LOS) stabilization system. It shows that the proposed controller has more excellent stabilization performance than that of the conventional PI-Lead controller. An H$\_$$\infty$/ control has been also applied to the system for reducing modeling errors and the settling time of the system. The LMI-based H$\_$$\infty$/ controller design is more practical in view of reducing a run-time than Riccati-based H$\_$$\infty$/ controller. This H$\_$$\infty$/ controller is available not only to decrease the gain in PI-Lead control, but also to compensate the identifications for the various uncertain parameters. Therefore, this paper, shows that the proposed LMI-based H$\_$$\infty$/ controller had good disturbance attenuation and reference input tracking performance compared with the control performance of the conventional controller under any real disturbances.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.565-568
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• 1995

This paper presents the position control of a closed-loop cylinder system using ER(electro-rheological)valve actuators. Following the field-dependent pressure analysis of the ER valve actuators on the basis of Bingham model of ER fluids, a 3 d.o.f. close-loop sylinder system having the heave, roll and pitch motions is proposed. The governing equations of motion are derived using Lagrange's equation, and a control model is established by considering system uncertain parameters such as load conditions. A sliding mode controller which has inherent robustness to system uncertainties is adopted to achieve robust tracking control performance. Tracking control results for sinusoidal trajectory were presented in order to demonstrate the effectiveness of the proposed control system.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.8
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• pp.1624-1632
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• 2009

In this study, we introduce the design methodology of an optimized Interval Type-2 fuzzy controller. The fixed MF design of type-1 based FLC leads to the difficulty of rule-based control design for representing the linguistically uncertain expression. In the Type-2 FLC as the expanded type of Type-1 FLC, we can effectively improve the control characteristic by using the footprint of uncertainty(FOU) of membership function. Type-2 FLC has a robust characteristic in the unknown system with unspecific noise when compared with Type-1 FLC. Through computer simulation as well as practical experiment, we compare their performance by applying both the optimized Type-1 and Type-2 fuzzy cascade controllers to ball and beam system. To evaluate each controller performance, we consider controller characteristic parameters such as maximum overshoot, delay time, rise time, settling time and steady-state error.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.20 no.8
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• pp.42-47
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• 2006

In this paper, we propose a sliding mode control with the bound estimation for robot manipulators without requiring exact knowledge of the robot dynamics. For the bound estimation, the upper bound of the uncertain nonlinearities of robot dynamics is represented as a Fredholm integral equation of the first kind and we propose an adaptive scheme which is only dependent on the sliding surface function. Also, we prove the asymptotic stability for the robot systems using two important properties in the robot dynamics: skew-symmetry and positive-definiteness of robot parameters.

• Management Science and Financial Engineering
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• v.6 no.2
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• pp.1-28
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• 2000

Supply chain management issues faced by a manufacturing company are considered in this paper. The supply chain consists of a manufacturing company and its suppliers. The manufacturer produces multiple products with inputs (e.g., raw materials) from the suppliers, but each product needs a different mix of these inputs. The market demand for the products is uncertain. We develop a mathematical model and algorithm, which can help the manufacturer to solve its procurement decision problem: how much of raw material to order from which supplier. The model incorporates such factors as market demand uncertainty, product's input requirement, supplier's as well as manufacturer's capacity, plus other costs comparable with those in a typical newsboy problem. Numerical examples are presented to see the interacting effects among critical parameters and variables.