• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2005년도 춘계 학술발표회 논문집
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• pp.201-208
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• 2005

Neuro-Fuzzy modeling approach is proposed to predict the dynamic behavior of a single-degree-of-freedom structure that is equipped with hybrid base isolation system. Hybrid base isolation system consists of friction pendulum systems (FPS) and a magnetorheological (MR) damper. Fuzzy model of the M damper is trained by ANFIS using various displacement, velocity, and voltage combinations that are obtained from a series of performance tests. Modelling of the FPS is carried out with a nonlinear analytical equation that is derived in this study and neuro-fuzzy training. Fuzzy logic controller is employed to control the command voltage that is sent to MR damper. The dynamic responses or experimental structure subjected to various earthquake excitations are compared with numerically simulated results using neuro-fuzzy modeling method. Numerical simulation using neuro-fuzzy models of the MR damper and FPS predict response of the hybrid base isolation system very well.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 제38회 하계학술대회
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• pp.1009-1010
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• 2007

This paper presents Fuzzy-Neuro PI controller of IPMSM drive using fuzzy and neural-network. In general, PI controller in computer numerically controlled machine process fixed gain. To increase the robustness, fixed gain PI controller, Fuzzy-Neuro PI controller proposes a new method based fuzzy and neural-network. Fuzzy-Neuro PI controller is developed to minimize overshoot and settling time following sudden parameter changes such as speed, load torque, inertia, rotor resistance and self inductance. The results on a speed controller of IPMSM are presented to show the effectiveness of the proposed gain tuner.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.236-236
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• 2000

Control of Industrial processes is very difficult due to nonlinear dynamics, effect of disturbances and modeling errors. M.Morari proposed Internal Model Control(IMC) system that can be effectively applied to the systems with model uncertainties and time delays. The advantage of IMC systems is their robustness with respect to a model mismatch and disturbances. But it was difficult to apply for nonlinear systems. Adaptive Neuro-Fuzzy Inference System which contains multiple linear models as consequent part is used to model nonlinear systems. Generally, the linear parameters in neuro-fuzzy inference system can be effectively utilized to identify a nonlinear dynamical systems. In this paper, we propose new IMC design method using adaptive neuro-fuzzy inference system for nonlinear plant. Numerical simulation results show that proposed IMC design method has good performance than classical PID controller.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
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• pp.845-848
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• 1993

This paper proposes a design method of fuzzy phase-lead compensator and its self-learning by neural network. The main feature of the fuzzy phase-lead compensator is to have parameters for effectively compensating phase characteristics of control systems. An important theorem which is related to phase-lead compensation is derived by introducing concept of frequency characteristics. We propose a design procedure of fuzzy phase-lead compensators for linear controlled objects. Furthermore, we realize a neuro-fuzzy compensator for unknown or nonlinear controlled objects by using Widrow-Hoff learning rule.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 B
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• pp.513-515
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• 1999

In this paper, Neuro-Fuzzy controller which has the characteristic of Fuzzy control and artificial Neural Network is designed A fuzzy rule to be applied is selected automatically by the allocated neurons. The neurons correspond to Fuzzy rules which are created by the expert. In order to adaptivity, the more precise modeling is implemented by error back propagation learning of adjusting the link-weight of fuzzy membership function in Neuro-fuzzy controller. The more classified fuzzy rule is used to include the property of Dual mode Method. To test the effectiveness of the algorithm designed above the experiment for DC servo motor with variable load as variable load plant is implementation.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 하계학술대회 논문집 D
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• pp.2018-2020
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• 2001

In this study, a Neuro-Fuzzy Controller which has the characteristic of Fuzzy control and Artificial Neural Network is designed. A fuzzy rule to be applied is automatically selected by the allocated neurons. The neurons correspond to Fuzzy rules are created by an expert. To adapt the more precise modeling is implemented by error back propagation learning of adjusting the link-weight of fuzzy membership function in the Neuro-Fuzzy controller. The more classified fuzzy rule is used to include the property of dual mode method. In order to verify the effectiveness of an algorithm designed above, an operating characteristic of a AC servo motor is investigated.

• 한국지능시스템학회논문지
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• 제12권2호
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• pp.144-150
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• 2002

본 논문에서는 지연시간을 갖는 비선형 시스템의 효율적 제어를 위해 퍼지-신경망에 기반한 지연시간 보상기를 제안하였다. 제안된 제어시스템은 ANFIS(Adaptive Neuro-Fuzzy Inference System)라고 불리는 두개의 퍼지-신경망으로 구성되며 이중 하나는 직-병렬 방식으로 동작하고 다른 하나는 병렬 방식으로 동작한다. 직-병렬 방식으로 동작하는 퍼지-신경망은 지연시간을 갖는 비선형 시스템의 응답을 추종하는 특성을 갖으며 병렬 방식으로 동작하는 퍼지-신경망은 지연시간을 보상하기 위한 시스템 출력을 예측하는 기능을 수행한다. 따라서 본 연구에서 제안된 시스템은 전형적인 Smith 예측기의 비선형 시스템에의 적용을 위한 확장이라고 생각할 수 있다. 본 논문에서는 제안된 지연시간 보상기의 상세한 설계과정을 보였으며 또한 제안된 제어기 설계 기법의 유용성 화인을 위해 비선형 수치데이터에 대한 컴퓨터 모의실험을 수행하였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2009년도 제40회 하계학술대회
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• pp.778_779
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• 2009

The maximum output torque developed by the machine is dependent on the allowable current rating and maximum voltage that the inverter can supply to the machine. Therefore, to use the inverter capacity fully, it is desirable to use the control scheme considering the voltage and current limit condition, which can yield the maximum torque per ampere over the entire speed range. The paper is proposed maximum torque control of induction motor drive using adaptive learning neuro fuzzy controller and artificial neural network(ANN). The control method is applicable over the entire speed range and considered the limits of the inverter's current and voltage rated value. For each control mode, a condition that determines the optimal d, q axis current $_i_{ds}$, $i_{qs}$ for maximum torque operation is derived. The proposed control algorithm is applied to induction motor drive system controlled adaptive learning neuro fuzzy controller and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper is proposed the analysis results to verify the effectiveness of the adaptive learning neuro fuzzy controller and ANN controller.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 추계학술대회 학술발표 논문집
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• pp.433-439
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• 1998

In this paper, we present an approach to the structure identification based on the input space partition methods and to the parameter identification by hybrid learning method in neuro-fuzzy system. The structure identification can automatically estimate the number of membership function and fuzzy rule using grid partition, tree partition, scatter partition from numerical input-output data. And then the parameter identification is carried out by the hybrid learning scheme using back-propagation and least squares estimate. Finally, we sill show its usefulness for neuro-fuzzy modeling to truck backer-upper control.

• 한국정보통신학회논문지
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• 제7권8호
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• pp.1759-1766
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• 2003

본 논문에서는 로봇 매니퓰레이터의 궤적 추종 제어에 관한 연구를 위하여 뉴로­퍼지 제어기를 제안하였다. 궤적 추종 제어기를 설계할 경우, 주로 이용되는 효과적인 방법은 토크 계산 제어 방식이다. 그러나, 로봇 매니퓰레이터에 의한 불확실성 문제로 인하여 토크 계산 제어 방식만으로는 좋은 제적 추종 성능을 얻을 수가 없다. 그러므로, 본 논문에서는 로봇 매니퓰레이터에서 발생한 불확실성을 보상하기 위하여 제안된 뉴로­퍼지 제어기를 이용하였다. 뉴로­퍼지 제어기에서의 퍼지 규칙의 수를 49개로 설정하였으며, 2관절 로봇 매니퓰레이터를 이용한 컴퓨터 시뮬레이션을 통해 제어기의 효율성을 입증하였다. 그 결과. 제안된 뉴로­퍼지 제어기의 출력이 로봇 매니퓰레이터에서 발생한 불확실성을 효과적으로 감소시킬 수 있음을 확인할 수 있었다.