• 한국정밀공학회지
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• 제15권5호
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• pp.19-27
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• 1998

Color Display Tube (CDT) used in computer monitors, consists of many components. Deflection Yoke(DY) among them supplies the vertical and horizontal magnetic fields so that the spatial trajectories of electron beams are deflected according to the synchronization signals. If the magnetic fields are not correctly formed, there will be color blurring or blooming by a mis-convergence of each beam and the color image on screen may not be clear. Therefore, in the manufacture of DY. its quality is strictly examined to get the desired convergence and the occurred mis-convergence can be cured by sticking ferrite sheets on the inner part of DY. However, because it needs expert's knowledge and experience to find the proper position of the sheet, this article introduces an intelligent controller that the knowledge-base represented by a neuro-fuzzy model is used to find the optimal position of the ferrite sheet for the convergence.

• 제어로봇시스템학회논문지
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• 제7권6호
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• pp.494-501
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• 2001

In this paper an ANFIS(Adativo Neuro-Fuzzy Inference System)- based fault detection and diagnosis for a closed loop control system is proposed. The proposed diagnostic system contains two ANFIS. One is run as a parallel model within the model in closed loop control(MCL) and the other is run as a series-parallel model within the process in closed loop(PCL) for the generation of relevant symptoms for fault diagnosis. These symptoms are further processed by another classification logic with simple rules and neural network for process and controller fault diagnosis. Experimental results for a DC shunt motor control system illustrate the effectiveness of the proposed diagnostic scheme.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2000년도 춘계학술대회논문집
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• pp.286-290
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• 2000

Optimal neuro-control algorithm is extended to the control of a multi-degree-of-freedom structure. An active mass driver(AMD) system on the top roof is used as an exciter. The control signals are made by a multi-layer perceptron(MLP) which is trained by minimizing a sub-optimal performance index. The performance index is a function of both the output responses and the control signals. Structure having nonlinear hysteretic behavior is also trained and controlled by using proposed control algorithm. In training neuro-controller, emulator neural network is not used. Instead, sensitivity-test data are used. Therefore, only one neural network is used for the control system. Both the time delay effect and the dynamics of hydraulic actuator are included in the simulation. Example shows that optimal neuro-control algorithm can be applicable to the multi-degree of freedom structures.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1994년도 추계학술대회 논문집
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• pp.414-419
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• 1994

In recent years, there has been an increasing intest in control of active automotive suspension systems with a goal of improving the ride comfort and safety. Many approaches for these purposes have used linearized models of the suspension's dynamics, allowing the use of linear control theory. However, the linearized model does not well descriibe the actual system behavior which is inherently nonlinear. The object of this study is to develop a neuro controlled active suspension for the ride quality improvement. After obtaining active control law using optimal control theory, we use the artificial neural network to train the neuro controller to learn the relation of road input and control force. Form the numerical results, we found that back propagation learning does show good pattern matching and vertical acceleration of the driver's seat and sprung mass.

• KIEE International Transaction on Systems and Control
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• 제12D권1호
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• pp.17-26
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• 2002

In this paper, a new design methodology of the fuzzy controller is presented. The performance of the fuzzy controller is sensitive to the variety of scaling factors. The design procedure is based on evolutionary computing (more specifically, a genetic algorithm) and estimation algorithm to adjust and estimate scaling factors respectively. The tuning of the soiling factors of the fuzzy controller is essential to the entire optimization process. And then we estimate scaling factors of the fuzzy controller by means of two types of estimation algorithms such as HCM (Hard C-Means) and Neuro-Fuzzy model[7]. The validity and effectiveness of the proposed estimation algorithm for the fuzzy controller are demonstrated by the inverted pendulum system.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 1999년도 추계학술대회 논문집 - 한국공작기계학회
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• pp.162-169
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• 1999

In this paper, it is presented a new scheme of adaptive-neuro control system to implement real-time control of robot manipulator using digital Signal Processors. Digital signal processors DSPs. are micro-processors that are particularly developed for variables. Digital version of most advanced control algorithms can be defined as sums and products of measured variables, thus it can be programmed and executed through DSPs. In addition, DSPs are as fast in computation as most 32-bit micro-processors and yet at a fraction of their prices. These features make DSPs a biable computatinal tool in digital implementation of sophisticated controllers. Unlike the well-established theory for the adaptive control of linear systems, there exists relatively little general theory for the adaptive control of nonlinear systems. Adaptive control technique is essential for providing a stable and robust performance for application of robot control. The proposed neuro control algorithm is one of learning a model based error back-propagation scheme using Lyapunov stability analysis method. The proposed adaptive-neuro control scheme is illustrated to be a efficient control scheme for implementation of real-time control of robot system by the simulation and experiment.

• 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.

• 한국우주과학회:학술대회논문집(한국우주과학회보)
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• 한국우주과학회 2011년도 한국우주과학회보 제20권1호
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• pp.32.2-32.2
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• 2011

State dependent Riccati equation (SDRE) control technique has been widely used in the control society. Although it solves nonlinear optimal control problems, which minimizes state error and control efforts simultaneously, it has drawbacks when it is to be applied to the real time systems in that it requires much computational efforts. So the real time system whose computational ability is limited (for example, satellites) cannot afford to use SDRE controller. To solve this problem, a hybrid controller which is based on MSDRE (Modified SDRE) and ANFIS (Adaptive Neuro-Fuzzy Inference System) has been proposed by Abdelrahman et al. (2010). We propose a hybrid controller based on SDRE and ANFIS, and apply the hybrid controller to the hardware attitude simulator to perform a HIL (Hardware-In-the-Loop) simulation. Through HIL simulation, it is demonstrated that the hybrid controller satisfies the control requirement and the computation load is reduced significantly. In addition, the effects of statistical properties of the ANFIS training data to the performance of the ANFIS controller have been analyzed.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 합동 추계학술대회 논문집 정보 및 제어부문
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• pp.416-419
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• 2001

In this paper a new approach to estimate scaling factors of fuzzy controllers such as the fuzzy PID controller and the fuzzy PD controller is presented. The performance of the fuzzy controller is sensitive to the variety of scaling factors[1]. The desist procedure dwells on the use of evolutionary computing(a genetic algorithm) and estimation algorithm for dynamic systems (the inverted pendulum). The tuning of the scaling factors of the fuzzy controller is essential to the entire optimization process. And then we estimate scaling factors of the fuzzy controller by means of two types of estimation algorithms such as Neuro-Fuzzy model, and regression polynomial [7]. This method can be applied to the nonlinear system as the inverted pendulum. Numerical studies are presented and a detailed comparative analysis is also included.

• 한국지진공학회논문집
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• 제9권2호통권42호
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• pp.7-15
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• 2005

본 연구에서는 하이브리드 면진장치가 설치된 단자유도 구조물의 동적거동을 예측할 수 있는 수치해석모델을 제안한다. 하이브리드 면진장치는 MR 감쇠기와 마찰진자시스템(FPS)으로 구성된다. MR감쇠기의 동적거동을 모형화하기 위하여 뉴로-퍼지 모델을 사용한다. 다양한 변위, 속도, 전압의 조합을 사용하여 MR 감쇠기의 성능실험을 수행한 후 얻어진 데이터를 이용하여 MR 감쇠기 뉴로-퍼지 모델을 ANFIS로 학습시킨다. FPS의 모형화는 본 연구에서 유도한 비선형 모델식에 근거하여 뉴로-퍼지 모형화방법을 사용하여 이루어진다. 본 연구에서는 MR 감쇠기로 전달되는 제어전압을 조절하기 위하여 퍼지논리제어기를 사용한다. 다양한 지진하중을 사용한 진동대 실험을 통하여 얻은 실험체의 동적응답과와 뉴로-퍼지 모형화방법을 사용한 수치해석의 결과를 비교한다. 뉴로-퍼지 모델을 사용하여 MR 감쇠기와 FPS를 모형화해서 수치해석을 수행한 결과 하이브리드 면진장치의 동적거동을 매우 정확하게 예측할 수 있었다.