• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.49 no.9
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• pp.749-757
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• 2021

In transportation missions using quadrotor, the payload may change the model parameters, such as mass, moment of inertia, and center of gravity. Moreover, if position of the payload is constantly changing during flight, the effect can adversely affect the control performances. To handle this issue, we suggest Model Reference Adaptive Control based on Linear Quadratic Regulator(LQR+MRAC) to compensate the uncertainty caused by payload. Firstly, the mathematical modeling with the fixed payload is derived. Second, Linear Quadratic Regulator (LQR) is used to design the reference model and baseline controller. Also, through the Stability method, Adaptive law is derived to estimate the model parameters. To verify the performance of proposed control scheme, we compared LQR and LQR+MRAC in situations where uncertainties exist. And, when the disturbance exist, the classic MRAC and proposed controller is compared to analyze the transient response and robustness.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 1985.04a
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• pp.80-83
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• 1985

• Journal of the Korean Institute of Intelligent Systems
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• v.5 no.3
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• pp.11-35
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• 1995

In this paper the fuzzy extension for the classical engineering mechanics problems is studied. The governing differential equation is derived for the buckling loads of the columns with uncertain mediums: the their own weight and the flexural rigidity. The columns with one typical end constraint(hinged1 clarnped/free) and the other finite rotational spring with fuzzy constant are considered in numerical examples. The vertex method is used to evaluate the fuzzy functions. The Runge-Kutta method and Determinant Search method are used to solve the differential equation and determine the buckling loads, respectively. The membership functions of the buckling load are calculated. The index of fuzziness to quantitatively describe the propagation of fuzziness is defined. According to the fuzziness of governing factors, the varlation of index of fuzziness for buckling load is investigated, and the sensitivity for the end constraints is analyzed.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.20 no.3
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• pp.53-61
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• 2006

This paper is proposed adaptive fuzzy-neuro controller for high performance of induction motor drive. The design of this algorithm based on fuzzy-neural network controller that is implemented using fuzzy control and neural network. This controller uses fuzzy nile as training patterns of a neural network. Also, this controller uses the back-propagation method to adjust the weights between the neurons of neural network in order to minimize the error between the command output and actual output. A model reference adaptive scheme is proposed in which the adaptation mechanism is executed by fuzzy logic based on the error and change of error measured between the motor speed and output of a reference model. The control performance of the adaptive fuzzy-neuro controller is evaluated by analysis for various operating conditions. The results of experiment prove that the proposed control system has strong high performance and robustness to parameter variation, and steady-state accuracy and transient response.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.44 no.5
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• pp.416-422
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• 2016

This paper presents preliminary performance analysis of a satellite formation flying testbed, which is under development by Astrodynamics and Control Laboratory, Department of Astronomy, Yonsei University. A model reference adaptive controller (MRAC) with a first-order reference model is chosen to enhance the response of reaction wheel system which is subject to uncertainties caused by unmodelled dynamics and measurement noise. In addition, an on-line parameter estimation (OPE) technique based on the least square is combined to eliminate the effect of angular measurement noise by estimating the moment of inertia. Both numerical simulations and hardware experiments with MRAC support the effectiveness and applicability of the adaptive control scheme, which maintains the tracking error below $0.25^{\circ}$ for the entire time span. However, the high frequency control input generated in hardware experiment strongly suggests design modifications to reduce the effect of deadzone.

• Journal of the Korean Institute of Telematics and Electronics
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• v.22 no.3
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• pp.11-15
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• 1985

A Novel design of MRAC for achieving independent tracking and regulation objectives by applying Pad e-Type approximation in the controller parameter estimation is presented. The design of the controller is done with the unknown plant parameters from the first. The result of the design is a simple control scheme with the reduction of estimation para-meters. The performance of rho proprosed contiol structure in tracking and regulation is compared with the other method by computer simulation.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.41 no.3
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• pp.39-46
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• 2004

This paper is proposed adaptive fuzzy-neural network(FNN) controller for the speed control of interior permanent magnet synchronous motor(IPMSM) drive. The design of this algorithm based on FNN controller that is implemented by using fuzzy control and neural network. This controller uses fuzzy rule as training patterns of a neural network. Also, this controller uses the back-propagation method to adjust the weights among the neurons of neural network in order to minimize the error between the command output and actual output. A model reference adaptive scheme is proposed in which the adaptation mechanism is executed by fuzzy logic based on the error and change of error measured between the motor speed and output of a reference model. The control performance of the adaptive FNN controller is evaluated by analysis for various operating conditions. The results of analysis prove that the proposed control system has strongly high performance and robustness in parameter variation, steady-state accuracy and transient response.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.894-896
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• 1999

PID 제어는 50년의 역사를 갖기 때문에 현장의 사용자는 이 제어방식에 익숙해져 있으며, 제어장치의 구성이 간단하며 제어기의 최적동조가 가능하므로 많은 분야에서 사용되고 있다[1]. 그러나 PID 제어기에 의해서 얻은 결과에 대하여 만족하기 위해서는 많은 시행착오를 겪어야 한다. 또한 만족하는 결과를 얻었다고 할지라도 외란, 플랜트의 동특성이 바뀌는 경우 시스템을 추종하지 못하기 때문에 파라미터를 재조정하여야 한다. 유전 알고리즘은 자연세계의 진화 현상에 기초한 계산모델로서 John Holland에 의해서 1975년에 개발된 전역적인 최적화 알고리즘이며[1][2], 비선형 고차원, 불연속, 다중모드, 노이즈 함수 등에 대하여 강건함을 보여주고, 복잡한 탐색 공간에서 최적 값을 스스로 발견하는 학습 능력을 갖는다. 이 방법은 재생산, 교배, 돌연변이를 통하여 최적해를 찾은 방법으로 1989년에 D. E. Goldgerg에 의해서 체계적으로 정리된 후 여러 분야에서 응용되고 있다[3][4]. 그러나 유전 알고리즘은 목적함수만을 이용하여 해집단을 탐색하기 때문에 숙련운전자가 원하는 제어 특성 명세인 상승시간, 정착시간, 초과량(oveshoot) 둥을 구체적으로 명시하여 제어에 반영할 수 없다. 또한, 유전 알고리즘은 입력 값이 크게 바뀔 경우 다른 시스템으로 인식하여 새로운 탐색을 수행하는 단점을 가지고 있다. 본 논문은 첫째, 기준모델을 도입하여 플랜트의 성능을 기준모델로 표현하여 플랜트가 요구하는 성능지표를 정량적으로 규정하는 것이 가능하였다. 또한, 이것은 미지 플랜트 동특성을 식별하기 위한 신호로 사용되어, 플랜트의 정보를 얻는데 이용되었다. 즉, 기준모델과 플랜트 출력사이의 추종 오차 정보가 적응기구인 PID 유전제어기의 입력으로 사용되며, 구형파 입력의 경우에도 기준모델과 플랜트의 출력차는 크게 변하지 않는다. 따라서, 유전 알고리즘의 목적함수에 기준 모델을 제안 적용하여 안정적이고, 세밀한 제어를 수행하였다. 둘째, PID의 간단하면서 확실한 제어가 가능하다는 점과 전역적인 최적값을 찾을 수 있는 유전 알고리즘을 적용하여 고속제어를 요하는 직류 서보 모터(DC Servo Motor) 운전 시 실시간 파라미터 동조에 적용하였다.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.05a
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• pp.574-577
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• 2000

The purpose of robot manipulator control is to make for manipulator take a trace of pre-planned trajectory. In this study, the algorithm of MRAC(Model Reference Adaptive Control) on reference to adaptive control theory was studied. The experiments were performed on 6-DOF robot manipulator with respect to p-d(proportional-differential) controller and adaptive controller. The property of adaptive control was studied and its efficiency proved by being compared to p-d controller.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.2
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• pp.109-114
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• 2001

본 논문에서는 비선형 제어시스템의 성능 개선을 위한 새로운 신경망 직접 적응제어 알고리즘을 제시하였다. 제어칙은 Gaussian RBF 신경망을 이용한 제어입력과 근사화 오차 및 외란의 영향을 제거하기 위한 보조제어 입력으로 구성하였다. 또한 신경망에 사용된 가중치와 보조입력의 파라미터를 조정하기 위한 적응칙은 Lyapunov 안정도 이론에 의하여 구하였다. 이렇게 함으로써 외란이나 근사화 오차에 관계없이 플랜트와 기준모델 사이의 오차가 0이 되도록 하는 알고리즘을 구할 수 있었다. 또한 제시된 알고리즘의 효용성을 알아보기 위하여 Duffing forced oscillation 시스템에 대하여 시뮬레이션 하여본 결과 만족할만한 성능을 얻을 수 있었다.