• 한국조명전기설비학회지:조명전기설비
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• 제10권5호
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• pp.83-93
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• 1996

본 연구는 부하변동이 갖은 유도 전송기 속도제어 시스템에 적합한 최소분산제어 알고리즘을 제안하고 구현을 실제적인 파라미터 추정방법을 제안한다. 그리고 고속 연산 신호처리요 프로세인 TMS 320C25를 이용한 제어 시스템을 구성한다. 적응칙은 선택적 망각인자를 갖는 순환형 최소자승법이 실제적 구현의 관점에서 언급되며, 일반적인 망각인자 알고리즘과 비교분석한다. 제어칙은 최소분산 제어 알고리즘으로 한다. 제어시스템은 알고리즘의 적용이 용이하도록 PC에 기초한 DSP제어 시스템으로 설계 제작한다. 시뮬레이션과 실험을 통하여, 본 연구의 초소분산제어 시스템이 부하변동에 강인한 구조를 갖고 있으며 유도전동기 제어에 실제적 구현이 가능함을 입증한다.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2002년도 전력전자학술대회 논문집
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• pp.455-458
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• 2002

A control of the body posture and movement is maintained by the vestibular system, vision, and proprioceptors. Especially, vestibular system has a very important function that controls the eye movement through vestibuloocular reflex and contraction of skeletal muscles through vestibulospinal reflex. However, postural disturbance caused by loss of vestibular function results in nausea, vomiting, vertigo and loss of craving for life. Lose of vestibular function leads to abnormal reflex of eye movements named nystagmus. Analysis of the nystagmus is needed to diagnose the vertigo, which is performed by means of electronystagmography (ENG). The purpose of this study is to develop a computerized system for data processing and an algorithm for the automatic evaluation of the slow component velocity (SCV) of nystagmus Induced by optokinetic(OKN) stimulation system. A new algorithm using recursive least square method (RLSM) to detect SCV of nystagmus is suggested in this paper. This method allows a fast and precise evaluation of the nystagmus, through artifact rejection techniques. The results are depicted in this paper.

• 전력전자학회논문지
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• 제9권1호
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• pp.42-49
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• 2004

본 논문은 데드비트 외란 관측기를 사용한 외부 부하 외란 보상과 파라미터 추정기에 의한 보상 이득의 조정을 나타내고 있다. 결론적으로 PMSM의 응답은 지표 시스템을 따른다. 부하 토크 보상 방법은 데드비트 관측기로 구성된다. 노이즈 영향을 감소시키기 위해 MA 처리에 의해 구현된 후단 필터를 적용하였고, RLSM 파라미터 추정기를 가진 파라미터 보상기가 주어진 실제 시스템의 이득 계산시 사용된 파라미터로 가상 동작하여 이득이 오차가 없는 것처럼 동작하게 한다. 제안된 추정기는 문제를 풀기 위해 고성능 외란 관측기와 조합하여 사용한다. 제안된 제어 시스템은 부하토크와 파라미터 변화에 대해 강인하고 정밀한 시스템이 된다. 이상의 제안된 시스템의 안정성과 유용함이 컴퓨터 시뮬레이션과 실험을 통하여 확인되었다.

• Journal of Power Electronics
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• 제8권4호
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• pp.354-362
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• 2008

This paper presents neural load torque compensation method which is composed of a deadbeat load torque observer and gains compensation by a parameter estimator. As a result, the response of the PMSM (permanent magnet synchronous motor) obtains better precision position control. To reduce the noise effect, the post-filter is implemented by a MA (moving average) process. The parameter compensator with an RLSM (recursive least square method) parameter estimator is adopted to increase the performance of the load torque observer and main controller. The parameter estimator is combined with a high performance neural load torque observer to resolve problems. The neural network is trained in online phases and it is composed by a feed forward recall and error back-propagation training. During normal operation, the input-output response is sampled and the weighting value is trained multi-times by the error back-propagation method at each sample period to accommodate the possible variations in the parameters or load torque. As a result, the proposed control system has a robust and precise system against load torque and parameter variation. Stability and usefulness are verified by computer simulation and experiment.

• International Journal of Aeronautical and Space Sciences
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• 제6권2호
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• pp.1-16
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• 2005

Adaptive control methods are studied for the Satellite to isolate vibration in spite of the nonlinear system dynamics and parameter uncertainties of disturbance. First, a centralized control scheme is developed based on the particle swarm optimization(PSO) algorithm and feedback theory to automatically tune controller gains. A simulation study of a 3 degree-of-freedom device was conducted to evaluate the performance of the proposed control scheme. Next, since a centralized control scheme is hard to construct model dynamics and not goad at performance when controller and systems environment are easily changed, a decentralized control scheme is presented to avoid these defects of the centralized control scheme from the point of view of production and maintenance. It is based on the adaptive control methodologies to find PID controller parameters. Experiment studies were conducted to apply the adaptive control scheme and evaluate the performance of the proposed control scheme with those of the conventional control schemes.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 추계학술대회 논문집 전력기술부문
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• pp.92-95
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• 2001

This study presents neural load disturbance observer that used to deadbeat load torque observer and regulation of the compensation gain by parameter estimator. As a result, the response of PMSM fellows that of the nominal plant. The load torque compensation method is compose of a neural deadbeat observer. To reduce of the noise effect, the post-filter, which is implemented by MA process, is proposed. The parameter compensator with RLSM(recursive least square method) parameter estimator is suggested to increase the performance of the load torque observer and main controller. The proposed estimator is combined with a high performance neural torque observer to resolve the problems. As a result, the proposed control system becomes a robust and precise system against the load torque and the parameter variation. A stability and usefulness, through the verified computer simulation, are shown in this paper.

• 전자공학회논문지T
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• 제35T권3호
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• pp.87-95
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• 1998

본 논문에서는 시스템의 차수가 고차이며 잡음과 시간지연이 있고 시스템의 파라미터가 변하는 비최소위상 시스템에 적응할 수 있는 자기동조 제어기의 설계 하중다항식 계수를 온라인으로 조정하는 방법을 제안한다. 일반화 최소분산 자기동조 제어기의 설계 하중다항식 계수 값은 확률 근사법인 Robbins-Monro알고리즘을 이용하여 온라인으로 얻으며 자기동조 제어기의 파라미터는 순환최소자승법으로 추정하였다. 제안한 자기동조 제어 방법은 다른 자기동조 제어 방법들11.21보다 간단하고 효과적이다. 제어 알고리즘의 타당성을 확인하기 위해 일정한 시간이 경과한 후 시스템의 파라미터가 변하고 시스템의 영점이 단위원 밖에 있는 고차 시스템에 대해 컴퓨터 시뮬레이션을 하였다.

• 대한전기학회논문지:전기기기및에너지변환시스템부문B
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• 제51권10호
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• pp.573-580
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• 2002

This paper presents neural load disturbance observer that used to deadbeat load torque observer and regulation of the compensation gain by parameter estimator As a result, the response of PMSM follows that of the nominal plant. The load torque compensation method is compose of a neural deadbeat observer. To reduce of the noise effect, the post-filter, which is implemented by MA process, is proposed. The parameter compensator with RLSM(recursive least square method) parameter estimator is suggested to increase the performance of the load torque observer and main controller. The proposed estimator is combined with a high performance neural torque observer to resolve the problems. As a result, the proposed control system becomes a robust and precise system against the load torque and the parameter variation. A stability and usefulness, through the verified computer simulation and experiment, are shown in this paper.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 D
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• pp.2949-2952
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• 2000

A new neural networks and learning algorithm are proposed in order to measure nonlinear heights of complexed road environments in realtime without pre-information. This new neural networks is Error Self Recurrent Neural Networks(ESRN), The structure of it is similar to recurrent neural networks: a delayed output as the input and a delayed error between the output of plant and neural networks as a bias input. In addition, we compute the desired value of hidden layer by an optimal method instead of transfering desired values by back-propagation and each weights are updated by RLS(Recursive Least Square). Consequently. this neural networks are not sensitive to initial weights and a learning rate, and have a faster convergence rate than conventional neural networks. We can estimate nonlinear models in realtime by ESRN and learning algorithm and control nonlinear models. To show the performance of this one. we control 7 degree of freedom full car model with several control method. From this simulation. this estimation and controller were proved to be effective to the measurements of nonlinear road environment systems.

• 반도체디스플레이기술학회지
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• 제7권1호
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• pp.17-22
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• 2008

The proposed hybrid controller consists of PID controller, feedforward controller and RLSE (Recursive Least Square Estimating) adaptive controller to compensate the force ripple that is periodic function of position in a linear motor. The modeling of force ripple is divided into the current-dependent and current-independent components. The current independent components never change as the current into the linear motor changes. On the other hand, the current-dependent components change as current varies when the velocity and load of the linear motor change. The proposed controller can compensate both force ripples. The feedforward controller compensates the current-independent components and the RLSE adaptive controller compensates the current-dependents components. We verified the performance of the controller by simulation and experiments.