• 대한교통학회지
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• 제18권5호
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• pp.57-67
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• 2000

본 논문에서는 추종 모형을 이용한 미시 교통류 시뮬레이션 모형(DETSIM)과 현장 조사 자료를 이용하여 단속류에서 링크의 통행시간을 추정하는 2개의 모형을 개발하였다. 2개의 모형은 통행시간과 교통변수가 가지는 비선형성에 적합하도록 퍼지논리 제어기를 이용하고 있다. 시뮬레이션의 수행결과와 현장 조사에 의하면 검지기로부터 발생하는 교통량, 점유율, 속도 자료중 링크의 통행시간을 가장 잘 반영하는 것은 점유율이며, 지점속도와 교통량은 부분적으로 통행시간에 대한 설명력을 가진다. 그러나, 통행시간을 추정하는데 적용되는 교통량, 점유율 및 지점속도는 동일한 교통상황에 대해서도 검지기의 위치에 따라 다른 값을 가지게 된다 본 연구에서는 이러한 문제를 극복하기 위한 2개의 통행시간 추정 모형을 개발하였으며. 이것은 교통량과 점유율을 이용하여 통행시간을 추정하는 모형(FETSYO)과 검지기로부터 발생되는 점유율과 지점속도를 이용하여 통행속도를 추정하는 모형(FETTOS)으로 구분된다. FETSVO모형은 이식성이 뛰어나며, FETTOS 모형은 신호주기와 녹색신호시간비 등의 자료가 요구되나, 통행시간을 직접추정하고 통행시간에 민감한 자료에 의하기 때문에 FETSVO모형보다 우수한 것으로 나타났다.

• 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.

• 한국정밀공학회지
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• 제25권12호
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• pp.89-99
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• 2008

In this paper, the position tracking control problem of the servo system with nonlinear dynamic friction is issued. The nonlinear dynamic friction contains a directly immeasurable friction state variable and the uncertainty caused by incomplete parameter modeling and its variations. In order to provide the efficient solution to these control problems, we propose the composite control scheme, which consists of the robust friction state observer, the FNN approximator and the approximation error estimator with sliding mode control. In first, the sliding mode controller and the robust friction state observer is designed to estimate the unknown internal state of the LuGre friction model. Next, the FNN estimator is adopted to approximate the unknown lumped friction uncertainty. Finally, the adaptive approximation error estimator is designed to compensate the approximation error of the FNN estimator. Some simulations and experiments on the servo system assembled with ball-screw and DC servo motor are presented. Results show the remarkable performance of the proposed control scheme. The robust friction state observer can successfully identify immeasurable friction state and the FNN estimator and adaptive approximation error estimator give the robustness to the proposed control scheme against the uncertainty of the friction parameters.

• 한국공작기계학회논문집
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• 제18권1호
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• pp.90-102
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• 2009

In this paper, a tracking control problem for a mechanical servo system with nonlinear dynamic friction is treated. The nonlinear friction model contains directly immeasurable friction state and the uncertainty caused by incomplete modeling and variations of its parameter. In order to provide the efficient solution to these control problems, we propose a hybrid control scheme, which consists of a robust friction state observer, a RFNN estimator and an approximation error estimator with sliding mode control. A sliding mode controller and a robust friction state observer is firstly designed to estimate the unknown infernal state of the LuGre friction model. Next, a RFNN estimator is introduced to approximate the unknown lumped friction uncertainty. Finally, an adaptive approximation error estimator is designed to compensate the approximation error of the RFNN estimator. Some simulations and experiments on the mechanical servo system composed of ball-screw and DC servo motor are presented. Results demonstrate the remarkable performance of the proposed control scheme.

• 한국정밀공학회지
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• 제12권4호
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• pp.28-38
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• 1995

The flank wear at the minor cutting edge significantly affects the geometric accuracy and surface roughness in finish machining. A fuzzy estimator based on a fuzzy inference algorithm with a max-min composition rule is introduced to evaluate the minor flank wear length. The features sensitive to minor flank wear are extracted from the dispersion analysis of a time series AR model of the feed directional acceleration signal. These features, dispersions, are used for constructing linguistic rules, and then the fuzzy inferences are carried out with test data sets collected under various cutting conditions. The proposed system turns out to be effective for estimating minor flank wear length.

• 한국정보전자통신기술학회논문지
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• 제7권3호
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• pp.132-136
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• 2014

본 논문에서는 주어진 데이터 전처리를 통한 새로운 형태의 TSK기반 퍼지 추론 시스템을 제안한다. 제안된 모델은 주어진 데이터의 효율적인 처리를 위해 클러스터링 기법인 Fuzzy C-Means 클러스터링 방법을 이용하였다. 제안된 새로운 형태의 퍼지추론 시스템의 전반부는 FCM 을 통하여 정규화된 멤버쉽 함수와 클러스터 수를 결정하기 때문에, 멤버쉽함수의 형태 및 개수를 정의할 필요가 없어, 모델의 구조 또한 간단한 형태를 이룬다. 본 논문에서 사용된 후반부는 4가지 형태로-간략추론, 1차선형추론, 2차선형추론, 변형된 2차선형추론-가 있으며, 이는 효율적인 후반부구조를 찾는데 주도적인 역할을 한다. 또한 제안된 모델의 후반부 파라미터 계수는 Weighted Least Squares Estimation(WLSE)을 사용하여 동정하며, Least Squares Estimation(LSE)를 적용한 모델의 성능과 비교한다. 마지막으로, Boston housing 데이터를 사용하여 제안된 모델의 성능을 평가하였다.

• 대한전기학회논문지:시스템및제어부문D
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• 제54권7호
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• pp.437-442
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• 2005

In order to realize the speed control of an induction motor, the information of the rotor speed is needed. So the speed sensor as an encoder or a pulse generator is used to obtain it. But the use of speed sensor occur the some problems in the control system of an induction motor. To solve the problems, the appropriate speed estimation algorithm is used instead of the speed sensor. Also there is the limitation to improve the speed control performance of an induction motor using the existing speed estimation algorithm. Therefore, in this paper, intelligent speed estimator using Fuzzy-Neural systems as adaptive laws in Model Reference Adaptive System is proposed so as to improve the existing estimation algorithm and ,using the rotor speed estimated by the Proposed estimator, the speed control of an induction motor without speed sensor is performed. The computer simulation and the experiment is executed to prove the performance of the speed control system usinu the proposed speed estimator.

• 한국감성과학회:학술대회논문집
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• 한국감성과학회 1998년도 춘계학술발표 논문집
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• pp.171-175
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• 1998

This work presents an estimation for stress status using biosignal and fuzzy theory. Stress is estimated by 'coin-build' experiment with two type, relax and stress status. The estimator uses five biosignals, fuzzy logic to combine these signals and physiological knowledge. The system was tested in 10 records of healthy indivisuals and acheived a template of a stress progress. This work presents an estimation for stress status using biosignal and fuzzy theory. Stress is estimated by 'coin-build' experiment with two type, relax and stress status. The estimator uses five biosignals, fuzzy logic to combine these signals and physiological knowledge. The system was tested in 10 records of healthy indivisuals and acheived a template of a stress progress.

• Journal of Electrical Engineering and Technology
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• 제4권1호
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• pp.66-78
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• 2009

This paper introduces the design of a fuzzy logic controller in conjunction with direct torque control strategy for a Permanent Magnet synchronous machine. A stator flux angle mapping technique is proposed to reduce significantly the size of the rule base to a great extent so that the fuzzy reasoning speed increases. Also, a fuzzy resistance estimator is developed to estimate the change in the stator resistance. The change in the steady state value of stator current for a constant torque and flux reference is used to change the value of stator resistance used by the controller to match the machine resistance.

• Wind and Structures
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• 제17권4호
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• pp.451-464
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• 2013

This study develops an extended inverse input estimation algorithm with intelligent adaptive fuzzy weighting to effectively estimate the unknown input wind load of nonlinear structural systems. This algorithm combines the extended Kalman filter and recursive least squares estimator with intelligent adaptive fuzzy weighting. This study investigated the unknown input wind load applied on a tower structural system. Nonlinear characteristics will exist in various structural systems. The nonlinear characteristics are particularly more obvious when applying larger input wind load. Numerical simulation cases involving different input wind load types are studied in this paper. The simulation results verify the nonlinear characteristics of the structural system. This algorithm is effective in estimating unknown input wind loads.