• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.756-761
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• 1998

This paper describes an in-line lossless data compression method using adaptive binary arithmetic coding. To achieve better compression efficiency , we employ an adaptive fuzzy -tuning modeler, which uses fuzzy inference to deal with the problem of conditional probability estimation. The design is simple, fast and suitable for VLSI implementation because we adopt the table -look-up approach. As compared with the out-comes of other lossless coding schemes, our results are good and satisfactory for various types of source data.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09b
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• pp.141-144
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• 2003

A composite adaptive dual fuzzy controller combining the approximate mathematical model, linguistic model description, linguistic control rules and identification modeling error into a single adaptive fuzzy controller is developed for a nonlinear system. It ensures the system output tracks the desired reference value and excites the plant sufficiently for accelerating the parameter estimation process so that the control performances are greatly improved. Using the Lyapunov synthesis approach, proposed controller is analyzed and simulation results verify the effectiveness of the proposed control algorithm.

• 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 Korean Institute of Information and Commucation Sciences Conference
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• 2002.05a
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• pp.518-522
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• 2002

This paper proposes a new application of Fuzzy logic to Variable Step Size Least Mean Square (VS-LMS) adaptive beamforming algorithm in CDMA systems. The proposed algorithm adjusts the step size of the Least Mean Square (LMS) by using the application of Fuzzy logic in which the increase or decrease of step size depends on the fuzzy inference results of the Mean Square Error (MSE). Computer simulation results show that the proposed algorithm has a better capacity of tracking compared with the conventional LMS algorithms and other variable step size LMS algorithms.

• Transactions on Control, Automation and Systems Engineering
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• v.3 no.1
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• pp.32-42
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• 2001

In this paper, a parameter estimator is developed for the plant model whose structure is represented by the Takagi-Sugeno model. The essential idea behind the on-line estimation is the comparison of the measured stated with the state of an estimation model whose structure is the same as that of the parameterized model. Based on the parameter estimation scheme, and indirect Model Reference Adaptive Fuzzy control(MRAFC) scheme is proposed to provide asymptotic tracking of a reference signal for the systems with uncertain for slowly time-varying parameters. The developed control law and adaptive law guarantee the boundedness of all signals in the closed-loop systems. In addition, the plant state tracks the state of the reference model asymptotically with time for any bounded reference input signal.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.5
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• pp.315-322
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• 2004

A direct adaptive state-feedback controller for highly nonlinear systems is proposed. This paper considers uncertain or ill-defined nonaffine nonlinear systems and employs a static fuzzy logic system (FLS). The employed FLS estimates. and adaptively cancels an unknown plant nonlinearity using its proved universal approximation property. A control law and adaptive laws for unknown fuzzy parameters and bounding constant are established so that the whole closed-loop system is stable in the sense of Lyapunov. The tracking error is guaranteed to be uniformly asymptotically stable rather than uniformly ultimately bounded with the aid of an additional robustifying control term. No a priori knowledge of an upper bound on an lumped uncertainty is required.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.40.5-40
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• 2001

In this paper, a new on-line parameter estimation methodology for the general continuous time Takagi-Sugeno(T-S) fuzzy model whose parameters are poorly known or uncertain is presented. An estimator with an appropriate adaptive law for updating the parameters is designed and analyzed based on the Lyapunov theory. The adaptive law is designed so that the estimation model follows the plant parameterized model. By the proposed estimator, the parameters of the T-S fuzzy model can be estimated by observing the behavior of the system and it can be a basis for the indirect adaptive fuzzy control.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.42 no.5 s.305
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• pp.13-18
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• 2005

Over the last few years, the control of bipedal robot has been considered a promising but difficult research field in the community of robotics. In this paper, a new robust output control method for a bipedal robot is proposed using the adaptive fuzzy logic. The adaptive fuzzy logic is used as an system approximator to cancel the unknown uncertainty. First, a model for a bipedal robot including switching leg influence, uncertainty and disturbance is presented. Second, a controller is designed in which the joint velocity measurement is not required. Fuzzy approximation error estimator is inserted in the system for tuning the fuzzy logic. Finally, the result of the computer simulation is presented to show the validity of the suggested control method.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.5
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• pp.452-461
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• 2007

Adaptive anti-sway and trajectory tracking control of overhead crane is presented, which utilizes Fuzzy Uncertainty Observer(FUO) and Fuzzy based Variable Structure Control(FVSC). We consider an overhead crane system which can be decoupled into the actuated and unactuated subsystems with its own lumped uncertainty such as parameter uncertainties and external disturbance. First, a new method for anti-sway control using FVSC is proposed to improve the conventional method based on Lyapunov direct method, while a conventional trajectory tracking control law using feedback linearization is directly adopted. Second, FUO is designed to estimate one of the two lumped uncertainties which can compensate both of them, based on the fact that two lumped uncertainties are coupled with each other. Then, an adaptive anti-sway control is proposed by incorporating the proposed FVSC and FUO. Under the condition that the observation error is Uniformly Ultimately Bounded(UUB) within an arbitrarily shrinkable region, the overall closed-loop system is shown to be Globally Uniformly Ultimately Bounded(GUUB). In addition, the Global Asymptotic Stability(GAS) of it is shown under the vanishing disturbance assumption. Finally, the effectiveness of the proposed scheme has been confirmed by numerical simulations.