• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.706-708
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• 1999

In this study, a seesaw system is used as an example to demonstrate effectiveness of the control theory. This problem which keeps a seesaw's balance is corresponded to one of regulating problems. In this regard, a controller is designed by LQ techniques and an observer is implemented and applied to estimate states which cannot be actually measured in this system. And the genetic algorithm is utilized to systematically choose weighting factors in the given cost function. The performance of the controller is verified through the experiment results.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.12
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• pp.2480-2483
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• 2009

This paper presents an LMI-based method to design a integral sliding mode controller for a class of uncertain time-delay systems. Using LMIs we derive an existence condition of a sliding surface guaranteeing the asymptotic stability of the sliding mode dynamics. And we give a switching feedback control law. Our method is a generalization of the previous integral sliding mode control design methods. Since our method is based on LMIs, it gives design flexibility for combining various useful design criteria that can be captured in the LMI-based formulation. We also give LMI existence conditions of sliding surfaces guaranteeing a-stability or LQ performance constraint. Finally, we give a numerical design example to show the effectiveness of the proposed method.

• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.446-448
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• 1997

The aim of this paper is to introduce a method designing the optimal model following boiler-turbine control system using genetic algorithms. This boiler-turbine control system is designed by applying genetic algorithms with reference model to the optimal determination of weighting matrices Q, R that are given by LQ regulator problem. These weighting matrices are optimized simultaneously in the search domain selected adequately. The effectiveness of this boiler-turbine control system is verified by computer simulation.

• The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.13 no.2
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• pp.234-234
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• 1999

The aim of this paper is to suggest a design method of the optimal model following control system using genetic algorithm (GA). This control system is designed by applying GA with reference model to the optimal determination of weighting matrices Q, R that are given by LQ regulator problem. The method to do this is that all the diagonal elements of weighting matrices are optimized simultaneously by GA, in the search domain selected adequately. And we design the model following control system to boi1er-turbine equipment by the proposed method. The model following control system designed by this method has the better command tracking performance than that of the control system designed by the trial-and-error method. The effectiveness of this control system is verified by computer simulation.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.43 no.4
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• pp.543-552
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• 1994

This paper presents a decentralized quadratic regulation architecture with feedforward neural networks for the control problem of complex systems. In this method, the decentralized technique was used to treat several simple subsystems instead of a full complex system in order to reduce training time of neural networks, and the neural networks' nonlinear mapping ability is exploited to handle the nonlinear interaction variables between subsystems. The decentralized regulating architecture is composed of local neuro-controllers, local neuro-identifiers and an overall interaction neuro-identifier. With the interaction neuro-identifier that catches interaction characteristics, a local neuro-identifier is trained to simulate a subsystem dynamics. A local neuro-controller is trained to learn how to control the subsystem by using generalized Backprogation Through Time(BTT) algorithm. The proposed neural network based decentralized regulating scheme is applied in the power System Stabilization(PSS) control problem for an imterconnected power system, and compared with that by a conventional centralized LQ regulator for the power system.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.346-352
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• 1998

In this paper, we give some geometric condition for a stochastic nonlinear system and we propose a control method for a stochastic nonlinear system using neural networks. Since a competitive learning neural networks has been developed based on the stochastic approximation method, it is regarded as a stochastic recursive filter algorithm. In addition, we provide a filtering and control condition for a stochastic nonlinear system, called perfect filtering condition, in a viewpoint of stochastic geometry. The stochastic nonlinear system satisfying the perfect filtering condition is decoupled with a deterministic part and purely semi martingale part. Hence, the above system can be controlled by conventional control laws and various intelligent control laws. Computer simulation shows that the stochastic nonlinear system satisfying the perfect filtering condition is controllable. and the proposed neural controller is more efficient than the conventional LQG controller and the canoni al LQ-Neural controller.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2167-2169
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• 2003

The LQG regulator presented in this dissertation is designed to improve system stability with the structure in which Kalman Filters and LQ regulator are inter-connected to avoid disturbances on the system. For the sake of performance evaluation of the LQG regulator in this dissertation, the regulator is put in use for the speed control of DC Servo Motors without speed sensors on them to realize speed control without sensors by estimating uncertain speed with its state signals and to construct an optimal controller using the LQG regulator. Furthermore, state estimation process with white noise entered onto the systems is been experimented through a series of simulation. As a consequence, the process of estimating speed signals out of the signals with white noise inputs is confirmed using Kalman Filter.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2267-2269
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• 2001

The looper control of hot strip finishing mill is one of the most important control item in hot strip rolling mill process. Loopers are placed between finishing mill stands and control the mass flow of the two stands. Another important action of the looper is to control the strip tension which influences on the width of the strip. So it is very important to control both the looper angle and the strip tension simultaneously but the looper angle and the strip tension are strongly interacted by each other. There are many control schemes such as conventional, non-interactive, LQ, LQG/LTR, and ILQ control in the looper control system. In this paper, we present the modeling for the looper of a hot strip finishing mill to control the tension of the strip and suggest the non-interactive(cross) and LQG/LTR control method.

• Transactions of the Korean Society of Automotive Engineers
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• v.22 no.1
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• pp.20-28
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• 2014

This paper presents static output feedback LQ and $H_{\infty}$ controllers for rollover prevention. Linear quadratic static output feedback controllers have been proposed for rollover prevention in such a way to minimize the lateral acceleration and the roll angle. Rollover prevention capability can be enhanced if $H_{\infty}$ controller is designed. To avoid full-state measurement for feedback requirement or sensitiveness of an observer to nonlinear model error, static output feedback is adopted. To design static output feedback controllers, Kosut's method is adopted because it is simple to calculate. Differential braking and active anti-roll bar are adopted as actuators that generate yaw and roll moments, respectively. The proposed method is shown to be effective in preventing rollover through the simulations on nonlinear multi-body dynamic simulation software, CarSim.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.51 no.3
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• pp.150-157
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• 2002

This paper presents preliminarily an implementation of digital high-performance motion control system of Reluctance Synchronous Motor (RSM) drives with direct torque control (DTC). The system consist of stator flux observer, torque estimator, two hysteresis band controllers, an optimal switching look-up table, IGBT voltage source inverter, and TMS320F240 DSP controller made by Texas Instruments. The stator fluff observer is based on the combined voltage and current model with stator flux feedback adaptive control, and the input of the observer are the stator voltage and current of motor terminal for wide speed range. The rotor position and speed sensor used 6000 pulse/rev encoder. In order to prove rightness of the suggested control algorithm, we have some simulation and actual experimental system at $\pm$20 and $\pm$2000 rpm. The developed digitally high-performance motion control system+ are shown a good response characteristic of control results and high performance features using 1.0kW RSM which has 2.57 Ld/Lq salient ratio.