• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.366-366
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• 2000

In this paper, an adaptive neuro-fuzzy filter using the conditional fuzzy c-means(CFCM) methods is proposed. Usualy, the number of fuzzy rules exponentially increases by applying the grid partitioning of the input space, in conventional adaptive neuro-fuzzy inference system(ANFIS) approaches. In order to solve this problem, CFCM method is adopted to render the clusters which represent the given input and output data. Parameter identification is performed by hybrid learning using back-propagation algorithm and total least square(TLS) method. Finally, we applied the proposed method to the nonlinear channel equalization problem and obtained a better performance than previous works.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.4
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• pp.228-234
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• 2005

In this paper, we proposed a self tuning adaptive fuzzy sliding control algorithms using gadient descent method to reduce chattering phenomenon which is viewed in variable control system. In design of FLC, fuzzy control rules are obtained from expert's experience and intuition and it is very difficult to obtain them. We proposed an adaptive algorithm which is automatically updated by consequence part parameter of control rules in order to reduce chattering phenomenon and simultaneously to satisfy the sliding mode condition. The proposed algorithm has the characteristics which are viewed in conventional VSC, e.g. insensitivity to a class of disturbance, parameter variations and uncertainties in the sliding mode. To demonstrate its performance, the proposed control algorithm is applied to an inverted pendulum system. The results show that both alleviation of chattering and performance are achieved.

• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.702-704
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• 1997

In this paper, we applied self-tuning controller with I-PD type to process with time delay's. Process parameters are estimated by the recursive least squares algorithm, and optimal gains are obtained. This paper shows self-tuning controller with I-PD type performs better than that of general PID type for the nonlinear system with sudden change of set-points.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.7
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• pp.605-613
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• 2001

In this paper, we design a robust adaptive controller for a nonlinear system with uncertainties to be rejected via disturbance adaptation law. The nonlinear system considered has unknown nonlinear functions being influenced by external disturbance. The upper bound of unknown nonlinear functions at each time is estimated by using a disturbance adaptation law. The estimated nonlinear functions are used to design a stabilizing function a control input. Tuning function is used to estimates unknown system parameter without overparametrization. A set-point regulation error converges to a residual set close to zero asymptotically. The effectiveness of the proposed controller is investigated by computer simulation.

• Proceedings of the KIPE Conference
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• 2013.07a
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• pp.506-507
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• 2013

본 논문은 단상 전원 시스템에서 입력전원의 위상각 추정에 2차 일반화 적분기(Second-Order Generalized Integrator - SOGI)를 기반으로 하는 적응 필터구조를 적용한다. SOGI 출력은 전원 위상각과 관련되고, 올바른 출력을 위해서는 중심 주파수 ${\omega}^{\prime}$이 전원 주파수를 빠르게 추정할 수 있도록 FLL(Frequency Locked Loop)제어가 필요하다. SOGI-FLL의 기존의 방법과는 다르게 비선형 특성이 강한 주파수 동기화 동특성 모델에 퍼지제어를 적용함으로써 복잡한 선형화 과정이 필요하지 않으며, 실시간 이득 조절로 빠르게 전원 주파수 추정을 할 수 있는데 이는 최종적으로 빠른 전원 위상각 추정을 의미한다. 제안된 방법에 대해서 시뮬레이션을 통하여 그 타당성을 검증한다.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.859-862
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• 1995

In this paper, an adaptive nonlinear observer using new Off-line algorithm is proposed to reduce the computing time. The estimated velocity data obtained from the control scheme is more accurate than that by the normal interpolation method when the velocity to be estimated is at the low speed or the fast speed. It is also shown that the adaptive controller based on AC100/C30 is useful for implementing the real-time controller.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.4
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• pp.271-278
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• 2005

In this paper, we present an adaptive observer for multi-output nonlinear systems that include unknown constant parameters and are not necessarily observable. Based on generalized nonlinear observer canonical form, new adaptive observer canonical form is proposed. Sufficient conditions are given for a nonlinear system to be transformed into the proposed adaptive observer canonical form. The existence of the proposed adaptive observer is given in terms of Lyapunov-like condition and SPR condition. An illustrative example is presented to show the design procedure of the proposed method.

• 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.

• The Journal of Korean Institute of Next Generation Computing
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• v.13 no.5
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• pp.39-50
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• 2017

Moving object tracking techniques can be considered as one of the most essential technique in the video understanding of which the importance is much more emphasized recently. However, irregularity of light condition in the video, variations in shape and size of object, camera motion, and occlusion make it difficult to tracking moving object in the video. Swarm based methods are developed to improve the performance of Kalman filter and particle filter which are known as the most representative conventional methods, but these methods also need to consider dynamic property of moving object. This paper proposes adaptive parameter control method which can dynamically change weight value among parameters in particle swarm optimization. The proposed method classifies each particle to 3 groups, and assigns different weight values to improve object tracking performance. Experimental results show that our scheme shows considerable improvement of performance in tracking objects which have nonlinear movements such as occlusion or unexpected movement.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.2 no.1
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• pp.71-77
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• 1998

In this Paper, the training principles and the control method of adaptive array antennas using the LMS and CMA algorithms for the $\pi$/4 QPSK signal is showed and the convergence characteristics, the adaptivity of directional pattern SINR and the replication of desired signal of adaptive arrays are compared and discussed each other. Computer simulation results showed that the SINR of LMS adaptive array was 13.8［dB］, and that of CMA was 12.8［dB］, and also the convergence characteristics of LMS was fast a little than that of CMA. The LMS adaptive array was also performed null point well for the interference signal direction than that of the CMA adaptive array.