• International Journal of Aeronautical and Space Sciences
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• v.18 no.2
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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.

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.3
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• pp.65-71
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• 1997

With the emergence of neural network, there is a revived interest in identification of nonlinear systems. So in this paper, to identify unknown nonlinear systems dynamically we propose DPNN(Dynamic Polynomial Neural Network) using GMDH (Group Method of Data Handling) algorithm. The dynamic system identification using GMDH consists of applying a set of inputloutput data to train the network by dynamically computing the necessary coeffici1:nt sets. Then, MRAC(Mode1 Reference Adaptive Control) is designed to control nonlinear systems using DPNN. In the result, we can see that the modelling and control using DPNN work well by computer simulation.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 1992.11a
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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.

• The Transactions of the Korean Institute of Power Electronics
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• v.9 no.5
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• pp.413-419
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• 2004

In the induction motor direct vector control system using the Gopinath model flux observer, the deterioration of the dynamic response due to the detuned rotor time constant is investigated. To solve this problem, the on line estimation algorithm of the rotor time constant using model reference adaptive control is proposed. The effect of the motor parameter variation on the rotor time constant estimation is analyzed through experiment. The estimation error due to the parameter variation converges within 5%. Thus applying the proposed algorithm to the Gopinath model flux observer, the robust direct vector control system of the induction motor to the parameter variation can be implemented.

• Journal of Electrical Engineering and information Science
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• v.2 no.6
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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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• v.17 no.4
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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.

• Proceedings of the KIPE Conference
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• 2003.07b
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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.

• Proceedings of the KIEE Conference
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• 1995.07b
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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.

• Journal of the Korean Institute of Telematics and Electronics
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• v.21 no.4
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• pp.78-83
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• 1984

A method is proposed for the adaptive control of linear time varying discrete systems. The stability problem of the model reference adaptive control systems is considered by means of the properties of hypergtability, The hyperstability approach also allows for solutions to the adaptation mechanism. Employing the principles of the continuous time case with output feedback renders it to the discrete case which simplified the system design. The system response is obtained by applying the ramp and step input as a reference signal to the system respectively. With checking the adaptation time for ramp and step input the validity of proposed algorithm was confirmed by the computer simulation.

• IEMEK Journal of Embedded Systems and Applications
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• v.7 no.5
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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.