• Advances in aircraft and spacecraft science
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• 제2권3호
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• pp.345-365
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• 2015

This paper focuses on the design, fabrication, testing and analysis of a novel load-bearing element with energy dissipation capability. A single element comprises two von-Mises trusses (VMTs), which are sandwiched between two plates and connected to dashpots that stroke as the VMTs cycle between stable equilibrium states. The elements can be assembled in-plane to form a large plate-like structure or stacked with different properties in each layer for improved load-adaptability. Also introduced in the elements are pre-loaded springs (PLSs) that provide high initial stiffness and allow the element to carry a static load even when the VMTs cannot under harmonic disturbance input. Simulations of the system behavior using the Simscape environment show good overall correlation with test data. Good energy dissipation capability is observed over a frequency range from 0.1 Hz to 2 Hz. The test and simulation results show that a two layer prototype, having one soft VMT layer and one stiff VMT layer, can provide good energy dissipation over a decade of variation in harmonic load amplitude, while retaining the ability to carry static load due to the PLSs. The paper discusses how system design parameter changes affect the static load capability and the hysteresis behavior.

• 대한전기학회논문지:전력기술부문A
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• 제48권3호
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• pp.278-285
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• 1999

This paper utilizes the method of Q-parameterization control to design a controller which solves the problem of imbalance in magnetic bearing systems. There are two methods to solve this problem using feedback controal. The first method is to compensate for the imbalance forces by generating opposing forces on the bearing surface (imbalance compensation). The second method is to make the rotor rotate around its axis of inertia (automatic balancing);in this case no imbalance forces will be generated. In this paper we deal with only imbalance compensation. The free parameter of the Q-parameterization controller is chosen such that these goals are achieved. After the introduction of a model of the magnetic bearing system, we explain the Q-parameterization controller design of the magnetic bearing system with emphasis on the rejection of sinusoidal disturbance for imbalance compensation design. The design objectives are formulated as a linear equations in the controller free paramete Q. Finally, simulation and experimental results are presented and showed the robustness and effectiveness of the proposed controllers.

• 한국산학기술학회논문지
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• 제9권6호
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• pp.1574-1580
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• 2008

본 논문에서는 외란이나 시스템의 파라미터 변동 및 불확실성 등이 존재하는 자동화 설비시스템을 강인하고 정밀하게 제어할 수 있도록 하기 위해 동적 신경망 처리기(DNP)인 신경망 제어기를 설계하였다. 자동화 설비시스템에서 부품의 조립, 가공 등 복잡하고 정교한 임무를 수행시키기 위해서는 end-effector의 이동경로 궤적에 대한 추적제어 뿐만 아니라 목표물에 대하여 접촉하는 힘의 궤적에 대한 추적 제어가 필수적이다. 또한 자동화 설비시스템에서 플랜트의 역기구학적인 좌표변환을 계산하기 위한 학습구조를 개발하였으며, DNP가 이용될 수 있는 예를 설명하였다. 제안된 동적 신경망인 DNP의 구조와 학습 알고리즘을 제시하고 컴퓨터 모의실험을 통해 학습 성능을 증명하였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2009년도 춘계학술대회 논문집 에너지변화시스템부문
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• pp.32-34
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• 2009

The field oriented control of induction motors is widely used in high performance applications. However, detuning caused by parameter disturbance still limits the performance of these drives. In order to accomplish variable speed operation, conventional PI-like controllers are commonly used. These controllers provide limited good Performance over a wide range of operation, even under ideal field oriented conditions. This paper is proposed model reference adaptive fuzzy control(MFC) and artificial neural network(ANN) based on the vector controlled induction motor drive system. Also, this paper is proposed control of speed and current using fuzzy adaption mechanism(FAM), MFC and estimation of speed using ANN. The proposed control algorithm is applied to induction motor drive system using FAM, MFC and ANN controller. Also, this paper is proposed the analysis results to verify the effectiveness of this controller.

• 한국조명전기설비학회:학술대회논문집
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• 한국조명전기설비학회 2006년도 춘계학술대회 논문집
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• pp.315-320
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• 2006

This paper presents speed control of IPMSM drive using neural network(NN) PI controller. In general, PI controller in computer numerically controlled machine process fixed gain. They may perform well under some operating conditions, but not all. To increase the robustness of fixed gain PI controller, NNPI controller proposes a new method based neural network. NNPI controller is developed to minimize overshoot, rise time and settling time following sudden parameter changes such as speed, load torque and inertia. Also, this paper is proposed speed control of IPMSM using neural network and estimation of speed using artificial neural network(ANN) controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The results on a speed controller of IPMSM are presented to show the effectiveness of the proposed gain tuner. And this controller is better than the fired gains one in terms of robustness, even under great variations of operating conditions and load disturbance.

• 한국조명전기설비학회:학술대회논문집
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• 한국조명전기설비학회 2009년도 춘계학술대회 논문집
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• pp.416-419
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• 2009

The widely used control theory based design of PI family controllers fails to perform satisfactorily under-parameter variation, nonlinear or load disturbance. In high performance applications, it is useful to automatically extract the complex relation that represent the drive behaviour. The use of loaming through example algorithms can be a powerful tool for automatic modelling variable speed drives. They can automatically extract a functional relationship representative of the drive behavior. These methods present some advantages over the classical ones since they do not rely on the precise knowledge of mathematical models and parameters. The paper proposes high performance speed and current control of synchronous reluctance motor(SynRM) drive using adaptive loaming mechanism-fuzzy neural network (ALM-FNN) and fuzzy logic control(FLC) controller. The proposed controller is developed to ensure accurate speed and current control of SynRM drive under system disturbances and estimation of speed using artificial neural network(ANN) controller. Also, this paper proposes the analysis results to verify the effectiveness of the ALM-FNN and ANN controller.

• 한국조명전기설비학회:학술대회논문집
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• 한국조명전기설비학회 2009년도 춘계학술대회 논문집
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• pp.404-407
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• 2009

The field oriented control of induction motors is widely used in high performance applications. However, detuning caused by parameter disturbance still limits the performance of these drives. In order to accomplish variable speed operation conventional PI-like controllers are commonly used. These controllers provide limited good performance over a wide range of operation even under ideal field oriented conditions. This paper is proposed adaptive fuzzy controller(AFC) and artificial neural network(ANN) based on the vector controlled induction motor drive system. Also, this paper is proposed control of speed and current using fuzzy adaptation mechanism(FAM), AFC and estimation of speed using ANN. The proposed control algorithm is applied to induction motor drive system using FAM, AFC and ANN controller. Also, this paper is proposed the analysis results to verify the effectiveness of this controller.

• 전력전자학회논문지
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• 제16권1호
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• pp.44-49
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• 2011

본 논문에서는 표면부착형 영구자석 동기전동기를 위하여 강인한 디지털 속도제어기를 제안한다. 제안된 속도제어기는 부하 토크관측기를 필요로 하지 않는 간단하며 부하 외란에 둔감한 디지털 제어 기법을 사용하므로 제어성능의 저하 없이 쉽고 단순하게 구현될 수 있다. 제안된 제어 알고리즘의 성능을 검증하기 위하여, 프로토타입 표면부착형 영구자석 구동시스템을 이용하여 시뮬레이션 및 실험을 하였다. 모터 파라미터 변동 하에서 수행된 시뮬레이션 및 실험결과를 통하여 제안된 기법이 표면부착형 영구자석 동기전동기의 속도를 정확하게 제어할 수 있음을 확인하였다.

• Journal of Power Electronics
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• 제18권1호
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• pp.92-102
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• 2018

A sliding mode control (SMC) for servo motors based on the differential evolution (DE) algorithm, called DE-SMC, is proposed in this study. The parameters of SMC should be designed exactly to improve the robustness, realize the precision positioning, and reduce the steady-state speed error of the servo drive. The main parameters of SMC are optimized using the DE algorithm according to the speed feedback information of the servo motor. The most significant influence factor of the DE algorithm is optimization iteration. A suitable iteration can be achieved by the tested optimization process profile of the main parameters of SMC. Once the parameters of SMC are optimized under a convergent iteration, the system realizes the given performance indices within the shortest time. The experiment indicates that the robustness of the system is improved, and the dynamic and steady performance achieves the given performance indices under a convergent iteration when motor parameters mismatch and load disturbance is added. Moreover, the suitable iteration effectively mitigates the low-speed crawling phenomenon in the system. The correctness and effectiveness of DE-SMC are verified through the experiment.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 제36회 하계학술대회 논문집 B
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• pp.1453-1455
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• 2005

This paper presents self tuning PI controller of IPMSM drive using neural network. Self tuning PI controller is developed to minimize overshoot, rise time and settling time following sudden parameter changes such as speed, load torque and inertia. Also, this paper is proposed speed control of IPMSM using neural network and estimation of speed using artificial neural network(ANN) controller. The results on a speed controller of IPMSM are presented to show the effectiveness of the proposed gain tuner. And this controller is better than the fixed gains one in terms of robustness, even under great variations of operating conditions and load disturbance.