• Journal of Institute of Control, Robotics and Systems
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• v.6 no.11
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• pp.1013-1021
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• 2000

This paper suggests fuzzy-PI controllers for a heavy-duty gas turbine. The fuzzy-PI controllers are designed to regulate rotor speed and exhaust temperature of the gas turbine. The controller gains are tuned by genetic algorithm(GA). This paper also proposes a new fitness function of GA using a desired output response. The suggested controller is compared with previous controllers via simulations and it is shown that the rotor speed variation of our controller is smaller than those of previous ones.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.10a
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• pp.347-348
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• 2006

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.135-137
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• 1995

A new design method is proposed for a fuzzy PD controller. By analyzing phase plane characteristics we can build and optimize the rule base of fuzzy logic controller. Also, a new gain tuning method is used to improve performance in the transient and steady state. The improved performance of the new methodology is shown by an application to the design of control system with a highly nonlinear actuator.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.163-166
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• 1999

In this paper, frequency synthesizer that can be used in IMT-2000 was designed and fabricated using dual loop PLL(Phase Locked Loop). For improving phase noise characteristic Voltage Controlled Oscillator was fabricated using coaxial resonator and eliminated frequency divider using SPD as phase detector and increased open loop gain. Fabricated frequency synthesizer had 1.82㎓ center frequency, 160MHz tuning range and -119.73㏈c/Hz low phase noise characteristic.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.2
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• pp.396-406
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• 2007

In this paper, we proposed development of improved model reduction and design of hierarchical controller using reduction model. The model reduction is considered that it is the transient response and the steady-state response through the use of nyquist curve. The hierarchical controller selected tuning of PID controller to ensure specified gain and phase margin and hybrid smith-predictor fuzzy controller using reduction model. Simulation examples are given to show the better performance of the proposed method than conventional methods.

• Proceedings of the KIEE Conference
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• 1988.07a
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• pp.902-904
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• 1988

For a generalized minimum variance controller algorithm the weighting polynomials are are calculated in a way to assign the closed loop poles of the system and to specify the controller gain at a frequency. As a result the oscillations in the control signal may be reduced without changing the deterministic behaviour of the system.

• Journal of Electrical Engineering and information Science
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• v.2 no.6
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• pp.14-19
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• 1997

In this paper, a new fuzzy sliding mode control algorithm is presented for trajectory control of robot manipulators. A fuzzy logic is applied to a sliding mode control algorithm to have the sliding mode gain adjusted continuously through fuzzy logic rules. With this scheme, te stability and the robustness of the proposed fuzzy logic control algorithm are proved and ensured by the sliding mode control law. The fuzzy logic controller requires only a few tuning parameters to adjust. Computer simulation results are given to show that the proposed algorithm can handle uncertain systems with large parameter uncertainties and external disturbances.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.7
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• pp.103-108
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• 1998

In this paper, we atempt to control the nonlinear power system for resistance spot welders to be a fully linearizesd system by applying the recently developed nonlinear feedback linearization techniques. the controller proposed in this paper is computationally simple. In addition, the easy gain tuning as well as the high dynamic performance of resistance spot welding systems can be obained simulaneously. To illustrate the dynamic performance of our controller further, we present some simulation results.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.8
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• pp.15-23
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• 1992

Neural network control has many innovative potentials for fast, accurate and intelligent adaptive control. In this paper, two kinds of neurocontrol architectures for the dynamic control of robot manipulators are developed. One is based on a System Identification and Control scheme and the other is based on the Feedback-Error leaming scheme. Both of the proposed architectures use an inverse dynamic neurocontroller in parallel with a linear neurocontroller. The difference is that the first architecture uses the system identifier to get the signals used for training neurocontrollers, while the second architecture uses a properly defined energy function. Compared with the previous types of neurocontrollers which are using an inverse dynamic neurocontroller and a fixed PD gain controller, the proposed architectures not only eliminate the painful process of the fixed gain tuning but also exhibit superior peformances because the linear neurocontroller can adapt its gains according to the applied task. This superior performance is tested and verified through computer simulation of the dynamic control of the PUMA 560 arm.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.7
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• pp.634-640
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• 2011

When a servo position controller of a robot or a driving units is composed of a PID controller, servomechanism which is modelled is composed of current, velocity and position control loops. After this model is simulated, the technique operating gain of each controller is suggested. The model consists of current, velocity and position controllers from the inside to the outside gradually. Also, to combine velocity and position controllers with 2 order system, simulation is performed after current controllers are composed, which are able for current loop to work ideally. If a current controller is treated with constant, it is possible for velocity and position controller to consist of controller into 2 order system. The technique is verified by applying T-company servo motor which is much more applied to current, velocity and position controller robots.