• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.2319-2324
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• 2005

This paper proposes a intelligent gain and boundary layer based sliding mode control (SMC) method for robotic systems with unknown model uncertainties. For intelligent gain and boundary layer, we employ the self recurrent wavelet neural network (SRWNN) which has the properties such as a simple structure and fast convergence. In our control structure, the SRWNNs are used for estimating the width of boundary layer, uncertainty bound, and nonlinear terms of robotic systems. The adaptation laws for all parameters of SRWNNs and reconstruction error bounds are derived from the Lyapunov stability theorem, which are used for an on-line control of robotic systems with unknown uncertainties. Accordingly, the proposed method can overcome the chattering phenomena in the control effort and has the robustness regardless of unknown uncertainties. Finally, simulation results for the three-link manipulator, one of the robotic systems, are included to illustrate the effectiveness of the proposed method.

• 한국지능시스템학회논문지
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• 제9권4호
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• pp.384-388
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• 1999

This paper describes an approach for synthesizing a modularized gain scheduled PD type fuzzy logic controller(FLC) of nonlinear magnetic bearing system where the gains of FLC are on-line adapted according to the operating point. Specifically the systematic procedure via root locus technique is carried out for the selection of the gains of FLC. Simulation results demonstrate that the proposed gain scheduled fuzzy logic controller yields not only maximization of stability boundary but also better control performance than a single operating point (without gain scheduling)fuzzy controller.

• 제어로봇시스템학회논문지
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• 제14권10호
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• pp.1014-1021
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• 2008

This paper proposes an intelligent sliding mode control method for robotic systems with the unknown bound of model uncertainties. In our control structure, the unknown bound of model uncertainties is used as the gain of the sliding controller. Then, we employ the function approximation technique to estimate the unknown nonlinear function including the width of boundary layer and the uncertainty bound of robotic systems. The adaptation laws for all parameters of the self-recurrent wavelet neural network and those for the reconstruction error compensator are derived from the Lyapunov stability theorem, which are used for an on-line control of robotic systems with model uncertainties and external disturbances. Accordingly, the proposed method can not only overcome the chattering phenomenon in the control effort but also have the robustness regardless of model uncertainties and external disturbances. Finally, simulation results for the five-link biped robot are included to illustrate the effectiveness of the proposed method.

• 한국ITS학회 논문지
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• 제9권2호
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• pp.41-50
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• 2010

본 논문에서는 다수의 이동통신 서비스를 위한 새로운 구조의 다중 대역 안테나를 설계 및 제작하였다. 제안된 안테나는 서로 다른 크기와 각도를 가지는 공동 접지된 두 개의 슬롯들에 의하여 다중 대역 안테나 특성을 가진다. 또한 안테나의 크기를 줄이고 이득을 향상시키기 위하여 삼면에 초크를 구성하였다. 안테나의 설계는 유한 적분법과 구조 근사법을 기본으로 하는 CST Microwave Studio 상용 프로그램을 사용하여 최적화한 후, 제작 및 측정을 하였다. 측정 결과 824 ~ 894 MHz와 1885 ~ 2500 MHz에서 반사계수가 -11 dB 이하(VSWR < 1.8)이며, 이득은 6 dBi 이상이었다.