• 제목/요약/키워드: model reference control

검색결과 1,163건 처리시간 0.026초

근적외선 분광분석법을 이용한 타우린의 정량 분석 (Quantitative Analysis of Taurine Using Near Infrared Spectrometry (NIRS))

  • 조창희;김효진;맹대영;서상훈;조정환
    • 약학회지
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    • 제42권6호
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    • pp.545-551
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    • 1998
  • Near Infrared transmittance Spectroscopy (NIRS) was used to evaluate and quantify the pharmaceutical active compounds. In the paper, taurine (2-Aminoethanesulfonic acid) was quantitatively analyzed in commercial pharmaceutical preparations. For calibration a central composite factorial design was used to determine concentrations of ingredients in reference samples. For the quantitative analysis of taurine, the most suitable data analysis method includes the calculation of second derivatives and a partial least squares regression (PLSR) model. By NIR spectrometry, combined with PLSR, the taurine concentration was successfully predicted with a relative standard error of prediction (SEP) lower than 1.04%.

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최적 변조제어기를 이용한 컨테이너 크레인의 안정화에 관한연구 (A Study on Stabilization of Container Cranes Using an Optimal Modulation Controller)

  • 허동렬
    • Journal of Advanced Marine Engineering and Technology
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    • 제23권5호
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    • pp.630-636
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    • 1999
  • In this paper in optimal modulation controller for position control and anti-sway of container crane systems is designed by a recursive algorithm that determines the state weighting matrix Q of a linear quadratic performance. The optimal modulation controller is based on optimal control. The basic feature of the recursive algorithm is the reduction of the number of iterations as well as minimization of the calculations involved So in order to obtain a mathematical model which rep-resents the equation of motion of the trolley and load Lagrange equation is used. The optimal modulation controller has been verified and simulated to show that it is robust when a load dis-turbance is applied and a reference is changed.

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회전 및 병진 흔들림 영상의 안정화 기법 (A Stabilization Method for Rotated and Translated Images)

  • 석호동;유준
    • 제어로봇시스템학회논문지
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    • 제12권8호
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    • pp.810-817
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    • 2006
  • This paper presents a rotational motion estimation and correction technique for digital image stabilization. An equivalent rotation model is derived so as to accommodate a combined rotational and the translational motion. Thanks to this simplification, the suggested estimation algorithm can directly find the rotational center using geometric characteristic of local motion vectors instead of using searching method. And we also present recursive version of frame to reference algorithm(FRA) for the real time implementation. The proposed DIS system does not require time consuming parameter searching process, while showing comparatively good performance compared with the previous ones. To show the effectiveness of the DIS scheme, the algorithm has been implemented on the DSP based hardware system and experimental results are also discussed.

MRAS 관측기를 이용한 SRM의 속도 및 위치센서없는 제어 (The Control of Switched Reluctance Motor Using MRAS without Speed and Position Sensors)

  • 양이우;김진수;김영석
    • 대한전기학회논문지:전기기기및에너지변환시스템부문B
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    • 제48권11호
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    • pp.632-639
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    • 1999
  • SRM(Switched Reluctance Motor) drives require the accurate position and speed information of the rotor. These informations are generally provided by a shaft encoder or resolver. High temperature, EMI, and dust may make detection performance deteriorate. Therefore, the elimination of the position and speed sensor is desirable. In this paper, a nonlinear adaptive observer using the MRAS(Model Reference Adaptive System) is proposed. The rotor speed and position are estimated by the adaptation law using the real and estimated currents. The stability of the adaptive observer is proved by Lyapunov stability theory. The proposed methods are implemented with TMS320C31 DSP. Experimental results prove that the observer has a good estimation performance of the rotor speed and position despite of the parameter variations and loads, and the speed control can be accomplished in the wide speed range.

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EMG Pattern Recognition based on Evidence Accumulation for Prosthesis Control

  • Lee, Seok-Pil;Park, Sand-Hui
    • Journal of Electrical Engineering and information Science
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    • 제2권6호
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    • pp.20-27
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    • 1997
  • We present a method of electromyographic(EMG) pattern recognition to identify motion commands for the control of a prosthetic arm by evidence accumulation with multiple parameters. Integral absolute value, variance, autoregressive(AR) model coefficients, linear cepstrum coefficients, and adaptive cepstrum vector are extracted as feature parameters from several time segments of the EMG signals. Pattern recognition is carried out through the evidence accumulation procedure using the distances measured with reference parameters. A fuzzy mapping function is designed to transform the distances for the application of the evidence accumulation method. Results are presented to support the feasibility of the suggested approach for EMG pattern recognition.

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비선헝 마찰 보상기를 이용한 램프추종 서보제어기에 관한 연구 (A study on the ramp tracking servo controller using nonlinear friction compensator)

  • 최승환;임동진
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 1998년도 추계학술대회 논문집 학회본부 B
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    • pp.426-428
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    • 1998
  • In this paper, a ramp tracking controller design method is proposed for the systems with nonlinear frictions. The objective is to design a controller which is capable of tracking a ramp reference input without steady state error. The controller is composed of a linear controller, integrators for error compensation, and a friction compensator. The compensator estimates the parameters of friction model. The friction parameters are estimated using two different method. Simulation and experimental results show that the proposed method is effective.

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신경 회로망을 이용한 BLDD 모터의 속도 적응 제어기 (Speed Control of BLDD Motor Using Neural Network based Adaptive Controller)

  • 김창균;이중휘;윤명중
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 1995년도 하계학술대회 논문집 B
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    • pp.714-716
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    • 1995
  • This Paper presents a novel and systematic approach to a self-learning controller. The proposed controller is built on a neural network consisting of a standard back propagation (BNN) and approxinate reasoning (AR). The fuzzy inference and knowledge representation are carried out by the neural network structure and computing, instead of logic inference. An architecture similar to that used by traditional model reference adaptive control system (MRAC) is employed.

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제어시스템을 위한 소프트웨어 패키지 통합화 기술 동향 (Computer-Aided Control Engineering (CACE) Framework Reference Model)

  • 이해문;정태진
    • 전자통신동향분석
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    • 제11권1호통권39호
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    • pp.49-64
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    • 1996
  • Computer-Aided Control Engineerig(CACE) 프레임워크는 컴퓨터 제어시스템의 개발비용과 신뢰성에 대한 위기에 대처하여, 제어시스템 엔지니어링 분야의 독립적인 소프트웨어 패키지들을 상호 호환성 있게 통합하여 컴퓨터 제어시스템의 모델링과 구현, 유지보수 및 소프트웨어 재사용에 이르는 라이프사이클 전단계에 걸쳐 일관적으로 사용될 수 있어야 한다. 이러한 개념의 개방형 통합 소프트웨어 패키지 운영체제가 광범위한 응용분야에서 재사용될 수 있도록 하기 위해서는 다음과 같은 5가지 서비스 클래스를 제공하는 프레임워크라는 기반구조에 의해 개발되어야 한다. 프레임워크에서 제공하려는 서비스들은 여러 응용분야의 소프트웨어 패키지들을 통합적으로 운영하기 위하여 필수적으로 요구되는 서비스 개념으로서 데이터베이스 서비스, 모델정의 서비스, 태스크 운영 서비스, 사용자 대화 서비스, 프로세스 통신을 위한 메시지 서비스들이 존재한다.

Lyapunov-based Fuzzy Queue Scheduling for Internet Routers

  • Cho, Hyun-Cheol;Fadali, M. Sami;Lee, Jin-Woo;Lee, Young-Jin;Lee, Kwon-Soon
    • International Journal of Control, Automation, and Systems
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    • 제5권3호
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    • pp.317-323
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    • 2007
  • Quality of Service (QoS) in the Internet depends on queuing and sophisticated scheduling in routers. In this paper, we address the issue of managing traffic flows with different priorities. In our reference model, incoming packets are first classified based on their priority, placed into different queues with different capacities, and then multiplexed onto one router link. The fuzzy nature of the information on Internet traffic makes this problem particularly suited to fuzzy methodologies. We propose a new solution that employs a fuzzy inference system to dynamically and efficiently schedule these priority queues. The fuzzy rules are derived to minimize the selected Lyapunov function. Simulation experiments show that the proposed fuzzy scheduling algorithm outperforms the popular Weighted Round Robin (WRR) queue scheduling mechanism.

AFLC를 이용한 IPMSM 드라이브의 NN 파라미터 추정 (Neural Network Parameter Estimation of IPMSM Drive using AFLC)

  • 고재섭;최정식;정동화
    • 전기학회논문지
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    • 제60권2호
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    • pp.293-300
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    • 2011
  • A number of techniques have been developed for estimation of speed or position in motor drives. The accuracy of these techniques is affected by the variation of motor parameters such as the stator resistance, stator inductance or torque constant. This paper is proposed a neural network based estimator for torque and stator resistance and adaptive fuzzy learning contrroller(AFLC) for speed control in IPMSM Drives. AFLC is chaged fuzzy rule base by rule base modifier for robust control of IPMSM. The neural weights are initially chosen randomly and a model reference algorithm adjusts those weights to give the optimum estimations. The neural network estimator is able to track the varying parameters quite accurately at different speeds with consistent performance. The neural network parameter estimator has been applied to slot and flux linkage torque ripple minimization of the IPMSM. The validity of the proposed parameter estimator and AFLC is confirmed by comparing to conventional algorithm.