• International Journal of Aeronautical and Space Sciences
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• 제18권2호
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• pp.290-299
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• 2017

Reconfigurable flight control using various kinds of adaptive control methods has been studied since the 1970s to enhance the survivability of aircraft in case of severe in-flight failure. Early studies were mainly focused on the failure of actuators. Recently, studies of reconfigurable flight controls that can accommodate complex damage (partial wing and tail loss) in conventional aircraft were reported. However, the partial wing loss effects on the aerodynamics of conventional type aircraft are quite different to those of BWB(blended wing body) aircraft. In this paper, a reconfigurable flight control algorithm was designed using a direct model reference adaptive method to overcome the instability caused by a complex damage of a BWB aircraft. A model reference adaptive control was incorporated into the inner loop rate control system enhancing the performance of the baseline control to cope with abrupt loss of stability. Gains of the model reference adaptive control were polled out using the Liapunov's stability theorem. Outer loop attitude autopilot was designed to manage roll and pitch of the BWB UAV as well. A 6-DOF dynamic model was built-up, where the normal flight can be made to switch to the damaged state abruptly reflecting the possible real flight situation. 22% of right wing loss as well as 25% loss for both vertical tail and rudder control surface were considered in this study. Static aerodynamic coefficients were obtained via wind tunnel test. Numerical simulations were conducted to demonstrate the performance of the reconfigurable flight control system.

• 한국지능시스템학회논문지
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• 제7권3호
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• pp.65-71
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• 1997

신경 회로망의 출현으로 비선형 시스템 모델링에 대한 관힘이 다시 고조되고 있다. 따라서 본 논문에서는 미지의 비선형 시스템을 동적으로 인식하기 위해 GMDH(Group Method of Data Handling) 일고리즘을 사용한 DPNN(Dynamic Polynomial Neural Network)을 제안한다. GMDH를 사용한 동적 시스템의 인신은 일렬의 입/출력 데이타를 인가하여 필요한 계수들의 집합을 동적으로 산출함으로써 훈련시킨다. 또한 DPNN을 이용하여 비선형 시스템을 제어하기 위해, MRA(Model Reference Adaptive Control)를 설계한다. 결과에서 컴퓨터 시뮬레이션을 통해 DPNN을 사용한 모델링과 제어가 잘 수행됨을 알 수 있었다.

• 한국조명전기설비학회:학술대회논문집
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• 한국조명전기설비학회 1992년도 추계학술발표회논문집
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• pp.41-46
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• 1992

고전적인 제어 기법들을 이용한 전동기의 속도 제어기는 하나의 고정된 동작점에 대해서 대개 양호한 동작 특성을 얻을 수 있으나 전동기 매개변수의 섭동 및 부하 외란의 존재시 규정된 제어 동작을 유지하기가 어렵다는 단점을 갖고 있다. 따라서 본 연구에서는 이러한 단점을 극복하기 위하여 적응 제어 기법중의 하나인 기준 모델 적응 제어 (Model Reference Adaptive Control : MRAC) 방식을 직류 전동기의 속도 제어에 적용하였으며 또한, 2차 이상인 전동기의 속도 제어 시스템을 1차로 저차화시켜 제어 알고리즘의 계산에 소요되는 시간을 줄임으로써 실시간 제어가 가능토록 하였다. 제시된 기준 모델 제어 기법과 PI 제어 기법을 직류 전동기의 속도 제어에 각각 적용하고 부하의 관성변화에 다른 속도 응답 특성을 실험을 통하여 비교 검토하였다.

• 전력전자학회논문지
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• 제9권5호
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• pp.413-419
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• 2004

고피나스 모델 자속관측기를 사용한 유도전동기 직접벡터제어 시스템에서 회전자 시정수에 오차가 있을 경우 시스템의 동특성이 저하되는 현상을 살펴보았으며, 이를 해결하기 위한 방안으로 MRAC를 이용한 회전자 시정수 실시간 추정 알고리즘을 제안하였다. 제안한 방식의 타당성을 검증하기 위해서 자속관측기에 사용되는 전동기 상수들의 오차가 회전자 시정수 추정에 미치는 영향을 실험적으로 분석한 결과 전동기 상수에 다소 오차가 있더라도 회전자 시정수의 추정오차는 5% 이내로 수렴됨을 보였다. 따라서 본 연구에서 제안한 회전자 시정수 추정 알고리즘을 고피나스 모델 자속관측기에 적용하게 되면 전동기 상수 변동에 강인한 유도전동기 직접벡터제어 시스템을 구현할 수 있다.

• Journal of Electrical Engineering and information Science
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• 제2권6호
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• pp.82-89
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• 1997

In this paper, an adaptive speed controller with an FNN(Feedforward Neural Network) is proposed for servo motor drives. Generally, the motor system has nonlinearities in friction, load disturbance and magnetic saturation. It is necessary to treat the nonlinearities for improving performance in servo control. The FNN can be applied to control and identify a nonlinear dynamical system by learning capability. In this study, at first, a robust speed controller is developed by Lyapunov stability theory. However, the control input has discontinuity which generates an inherent chattering. To solve the problem and to improve the performances, the FNN is introduced to convert the discontinuous input to continuous one in error boundary. The FNN is applied to identify the inverse dynamics of the motor and to control the motor using coordination of feedforward control combined with inverse motor dynamics identification. The proposed controller is developed for an SR motor which has highly nonlinear characteristics and it is compared with an MRAC(Model Reference Adaptive Controller). Experiments on an SR motor illustrate te validity of the proposed controller.

• Journal of Power Electronics
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• 제17권4호
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• pp.1058-1070
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• 2017

This paper proposes a novel active damping scheme to suppress LCL-filter resonance with only grid-current feedback control in grid-connected voltage-source converters. The idea comes from the concept of the model reference adaptive control (MRAC). A detailed theoretical derivation is given, and the effectiveness of this method is explained based on its physical nature. According to the control structure of this method, the active damping compensator, which is essentially a second order resonant integrator (SORI) filter, provides an effective solution to damp LCL resonance and to eliminate the need for additional sensors. Compared with extra feedback methods, the cost and complexity are reduced. A straightforward tuning procedure for the active damping method has been presented. A stability analysis is illustrated in the discrete domain while considering a one-step delay. Finally, experimental results are presented to validate the analysis and to demonstrate the good performance of the proposed method.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2003년도 춘계전력전자학술대회 논문집(2)
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• pp.900-903
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• 2003

An identification method of induction motor parameters such as rotor time constant and mutual inductance at standstill condition is discussed assuming that stator resistance and leakage has already been obtained applying two different DC voltage and single phase voltage to the induction motor, respectively. This proposed scheme is implemented by means of Model Reference Adaptive Control (MRAC) technique, which uses a rotor flux equation in voltage model as a reference model and one in current model and is demonstrated through experiment.

• 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.

• 대한전자공학회논문지
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• 제21권4호
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• pp.78-83
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• 1984

Hyperstability 이론에 의한 연속시간 시스템의 제어 알고리즘을 이산시간 선형시변 시스템의 적응제어에 직접 이용하여 시스템의 설계를 간단하게 하였다. 기준인력으로서 계단함수와 램프함수를 인가하여 설계된 시스템의 동작특성을 조사해 본 결과 공정의 출력이 모델의 출력에 적응함을 알 수 있었다.

• 대한임베디드공학회논문지
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• 제7권5호
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• pp.267-275
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• 2012

In this paper, we present the model-based autonomic computing framework for a cyber-physical system which provides a self-management and a self-adaptation characteristics. A development process using this framework consists of two phases: a design phase in which a developer models faults, normal status constrains, and goals of the CPS, and an operational phase in which an autonomic computing engine operates monitor-analysis-plan-execute(MAPE) cycle for managed resources of the CPS. We design a hierachical architecture for autonomic computing engines and adopt the Model Reference Adaptive Control(MRAC) as a basic feedback loop model to separate goals and resource management. According to the GroundVehicle example, we demonstrate the effectiveness of the framework.