• 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 Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2002.05a
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• pp.121-126
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• 2002

We proposed the design of SVC adaptive fuzzy logic controller(AFLC) using Tabu search and neural network. We tuned the gains of input-output variables of fuzzy logic controller(FLC) and weights of neural network using Tabu search. Neural network was used for adaptively tuning the output gain of FLC. The weights of neural network was learned from the back propagation algorithm in real-time. To evaluate the usefulness of AFLC, we applied the proposed method to single-machine infinite system. AFLC showed the better control performance than PD controller and GAFLC[8] for. three-phase fault in nominal load which had used when tuning AFLC. To show the robustness of AFLC, we applied the proposed method to disturbances such as three-phase fault in heavy and light load. AFLC showed the better robustness than PD controller and GAFLC[8].

• 유공압시스템학회:학술대회논문집
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• 2010.06a
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• pp.81-85
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• 2010

This paper deals with the issue of motion control of a single rod cylinder-load system using simple adaptive control (SAC) method. Prior to controller design, the experiment of open-loop response has been performed. Based on it, design parameters of transfer function are obtained. The effect of parallel feedforward compensator has been investigated by computer simulation, suppressing the oscillatory motion. Through experiments it is conformed that the SAC method gives good tracking performance compared to the PD control method.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.1
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• pp.38-44
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• 2001

This paper investigates the direct adaptive control of nonlinear systems using RBFN(radial basis function networks). The structure of the controller consists of a fixed PD controller and a RBFN controller in parallel. An adaptation law for the parameters of RBFN is developed based on the Lyapunov stability theory to guarantee the stability of the overall control system. The filtered tracking error between the system output and the desired output is shown to be UUB(uniformly ultimately bounded). To evaluate the performance of the controller, the proposed method is applied to the trajectory contro of the two-link manipulator.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.51 no.4
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• pp.188-195
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• 2002

We proposed the design of SVC adaptive fuzzy logic controller(AFLC) using Tabu search and neural network. We tuned the gains of input-output variables of fuzzy logic controller(FLC) and weights of neural network using Tabu search. Neural network was used for adaptively tuning the output gain of FLC. The weights of neural network was learned from the back propagation algorithm in real-time. To evaluate the usefulness of AFLC, we applied the proposed method to single-machine infinite system. AFLC showed the better control performance than PD controller and GAFLS[10] for three-phase fault in nominal load which had used when tuning AFLC. To show the robustness of AFLC, we applied the proposed method to disturbances such as three-phase fault in heavy and light load. AFLC showed the better robustness than PD controller and GAFLC[10].

• Journal of Korean Port Research
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• v.14 no.3
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• pp.291-301
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• 2000

T his paper presents the lateral and longitudinal control algorithm for the driving of a 4WS AGV(Automated Guided Vehicle). The control law to the lateral and longitudinal control of the AGV includes adaptive agin tuning ability, that is the controller gain of the gravity compensated PD controller can be changed on a real-time. The gain tuning law is derived from the Lyapunov direct method using the output error of the reference model and the actual model, And to show the performance of the presented lateral and longitudinal control algorithm, we simulate toe nonlinear AGV equations of the motion by deriving the Newton-Euler Method, The read path is from quay yard area to docking position in loading yard area. The quay yard area is where the quay crane loads the container to the AGV and the docking position is where the container is transferred to the gantry crane. The road types are constructed in a straight line and J-turn. When driving the straight line, the driving velocity is 6㎧ and the J-turn is 3㎧.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.1640_1641
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• 2009

Dynamic friction and force ripple are the most predominant factors that affect the positioning accuracy of permanent magnet synchronous motor(PMSM) servo drives system, and it is desirable to compensate them in finite time with a continuous control law. In this paper, based on LuGre dynamic friction model, a robust adaptive skidding mode controller is proposed to compensate the nonlinear effect of friction and force ripple. The controller scheme consists of a PD component and a robust adaptive sliding mode controller for estimating the unknown system parameter. Using Lyapunov stability theorem, asymptotic stability analysis and position tracking performance are guaranteed. Simulation results well verify the feasibility and the effectiveness of the proposed scheme for high0precision motion trajectory tracking.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.1
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• pp.102-108
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• 2008

This paper presents robust and adaptive control method for complicated three dimensional crane systems with uncertain effect. We consider an overhead crane system in which a trolly located on its top is moved to x- and y-axis independently. We first approximate the complicated crane model through linearization approach to simply construct a PD control and then design an adaptive control system for compensating modeling error and control deviation which is feasibly occurred due to system perturbation in practice. An adaptive control scheme is analytically derived using Lyapunov stability theory for a given bound of system perturbation. We accomplish numerical simulation for evaluation of the proposed control system and demonstrate its superiority comparing with the traditional control strategy.

• Proceedings of the KIEE Conference
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• 2000.07a
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• pp.34-36
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• 2000

This paper proposes the design of SVC adaptive fuzzy logic controller(AFLC) using Tabu search and neural network. We tuned the gain of input-output variables of fuzzy logic controller and weights of neural network using Tabu search. Neural network used to tune the output gain of FLC adaptively. We have weights of neural network learned using back propagation algorithm. We performed the nonlinear simulation on an single-machine infinite system to prove the efficiency of the proposed method. The proposed AFLC showed the better performance than PD controller in terms of the settling time and damping effect, for power system operation condition.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.49 no.5
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• pp.205-211
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• 2000

This paper presents an optimal design method for fuzzy logic controller (FLC) of HVDC using an Adaptive Evolutionary Algorithm(AEA). We have proposed the AEA which uses a genetic algorithm (GA) and an evolution strategy (ES) in an adaptive manner in order to take merits of two different evolutionary algorithms. The AEA is used for tuning fuzzy membership functions and scaling constants. Simulation results show that disturbances are well damped and the dynamic performances of FLC have better responses than those of PD controller when AC system load changes suddenly.