• Proceedings of the KIEE Conference
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• 2004.04a
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• pp.173-176
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• 2004

This paper is proposed to position and speed control of interior permanent magnet synchronous motor(SPMSM) drive without mechanical sensor. A adaptive fuzzy controller is applied for speed control of SPMSM drive A adaptive state observer is used for the mechanical state estimation of the motor. The observer was developed based on nonlinear model of SPMSM, that employs a d-q rotating reference frame attached to the rotor. A adaptive observer is implemented to compute the speed and position feedback signal. The validity of the proposed sensorless scheme is confirmed by various response characteristics.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.1775-1776
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• 2008

This paper presents a adaptive fuzzy control suitable for motion control of multi-link robot manipulators with uncertainties. When joint velocities are available, full state adaptive fuzzy feedback control is designed to ensure the stability of the closed loop dynamic. If the joint velocities are not measurable, an observer is introduced and an adaptive output feedback control is designed based on the estimated velocities. To reduce the number of fuzzy rules of the fuzzy controller, we consider the properties of robot dynamics and the decomposition of the unknown input gain matrix. The proposed controller is robust against uncertainties and external disturbances. The validity of the control scheme is demonstrated by computer simulations on a two-link robot manipulator.

• Proceedings of the KIPE Conference
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• 2003.11a
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• pp.186-189
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• 2003

This paper is proposed to position and speed control of interior permanent magnet synchronous motor(IPMSM) drive without mechanical sensor. A gopinath observer is used for the mechanical state estimation of the motor. The observer was developed based on nonlinear model of IPMSM, that employs a d-q rotating reference frame attached to the rotor, A gopinath observer is implemented to compute the speed and position feedback signal. The validity of the proposed scheme is confirmed by various response characteristics.

• Proceedings of the KIEE Conference
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• 1999.11b
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• pp.160-162
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• 1999

This paper presents an optimal design method for fuzzy controllers of HVDC system using adaptive evolutionary algorithm(AEA). We have proposed an adaptive evolutionary algorithm which uses a genetic algorithm and an evolution strategy in an adaptive manner in order to take merits of two different evolutionary computations. AEA is used for tuning fuzzy membership functions, scaling constants and PD gains. The simulation results show that the disturbances are well damped by both controllers and the dynamic performances of fuzzy controllers have better responses than those of PD controllers when mechanical torque changes suddenly.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2064-2066
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• 2002

This paper proposes Model Reference Adaptive Fuzzy System(MRAFS) using Fuzzy Logic Controller(FLC) as a adaptive laws in Model Reference Adaptive System(MRAS) in order to realize the speed-sensorless control of an induction motor. MRAFS estimates the speed of an induction motor with a rotor flux of a reference model and adjustable model in MRAS. Fuzzy logic controller reduces the error of the rotor flux between the reference model and the adjustable model using the error and the change of error as the input of FLC. The computer simulation is executed to verify the propriety and the effectiveness of the proposed system.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.632-634
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• 1998

This paper presents experimental results of the fuzzy controller for DC servo motor. The fuzzy controller consists of 9 quantized levels and 25 fuzzy rules. The fine Controller is employed in the fine control mode when the value of error is between -0.03 and +0.03, whereas the coarse controller is used in the coarse control mode when the value of error is in the outside range of -0.03 and +0.03. The experimental results show that the fuzzy controller provides a better performance (lower overshoot and error) than the PID controller regardless of the load applied.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.693-695
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• 1998

Fuzzy controller design consists of intuition, and any other information about how to control system, into a set of rules. These rules can then be applied to the system. It is very important to decide parameters of IF-THEN rules. Because fuzzy controller can make more adequate force to the plant by means of parameter optimization, which is accomplished by learning procedure. In this paper, we apply fuzzy controller designed to the inverted pendulum.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.696-698
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• 1998

Fuzzy controller design consists of intuition, and any other information about how to control system, into a set of rules. If the parameters of membership function in premise part and consequent part are set adequately, the controller designed can control plant well. But, if the parameters of function are set inadequately, the controller can't control well. So we must modify parameters using adaptive learning procedure. In this paper, we design adaptive fuzzy controller, and then verify its robustness.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.111-114
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• 1996

Hydroponics is to grow plants, not in soil but in water which the quantity of necessary chemical food can be controlled. In this paper, this is designed in the automatic system. The closed culture reduces cost of production and produces a many kinds of agricultural products in a confined place. An adaptive fuzzy control in the best method to solve and to overcome parametric uncertainties and non-linearity of the controlled system. A hydroponics automation system which is able to overcome these control problems. It is used in implementation of the hydroponics automation system. The performance is analyzed through an experiment in which the new adaptive fuzzy control method is applied to the automatic control of tomato hydroponics.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.158-161
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• 1996

A robust adaptive tracking control architecture is proposed for a class of continuous-time nonlinear dynamic systems for which an explicit linear parameterization of the uncertainty in the dynamics is either unknown or impossible. The architecture employs an adaptive fuzzy system to compensate for the uncertainty of the plant. In order to improve the robustness under approximation errors and disturbances, the proposed architecture includes deadzone in adaptation laws. Unlike the previously proposed schemes, the magnitude of approximate errors and disturbances is not required in the determination of the deadzone size, since it is estimated using the adaptation law. The proposed algorithm is proven to be globally stable in the Lyapunov sense, with tracking errors converging to the proposed architecture.