• 전자공학회논문지B
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• 제29B권10호
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• pp.37-45
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• 1992

For a precise manipulator control in the presence of environmental uncertainties, it has long been recognized that the robot should be controlled in a task-referenced space. In this respect, an effective visual servo control system for robot manipulators based on neural networks is proposed. In the proposed control system, a Backpropagation neural network is used first to learn the mapping relationship between the robot's joint space and the video image space. However, in the real control loop, this network is not used in itself, but its first and second derivatives are used to generate servo commands for the robot. Second, and Adaline neural network is used to identify the approximately linear dynamics of the robot and also to generate the proper joint torque commands. Computer simulation has been performed demonstrating the proposed method's superior performance. Futrhermore, the proposed scheme can be effectively utilized in a robot skill acquisition system where the robot can be taught by watching a human behavioral task.

• 제어로봇시스템학회논문지
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• 제11권5호
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• pp.429-434
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• 2005

In this paper, the maze navigation system using image recognition for autonomous mobile robot is proposed. The proposed maze navigation system searches the target by image recognition method based on ADALINE neural network. The infrared sensor system must travel all blocks to find target because it can recognize only one block information each time. But the proposed maze navigation system can reduce the number of traveling blocks because of the ability of sensing several blocks at once. Especially, due to the simplicity of the algorithm, the proposed method could be easily implemented to the system which has low capacity processor.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2019년도 추계학술대회
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• pp.136-137
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• 2019

본 논문에서는 ADALINE (Adaptive-Linear-Neuron) 구조의 ANN(Artificial Neural Network)을 이용한 3상 PWM 컨버터의 개방고장 진단 방법에 대해 제안한다. 3상 PMW 컨버터에서 스위치의 개방고장이 발생한 경우 보호회로에 의해 시스템이 중단되지 않으며, 개방고장으로 인한 상전류의 고조파와 직류 성분에 의해 주변 기기에 고장에 의한 파급효과가 나타날 수 있다. 이에 본 논문에서는 ADALINE을 이용하여 각 상의 THD(Total Harmonics Distortion)와 직류 성분 얻고 대소비교를 통해 개방고장이 발생한 스위치를 진단하는 방법에 대해 제안한다.

• Journal of Power Electronics
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• 제11권5호
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• pp.751-758
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• 2011

This paper presents an effective robust predictive control scheme for the active power filter (APF) using a smith-predictor based current regulator, which show superior features when compared to proportional-integral (PI) controllers in terms of an enhanced closed-loop bandwidth and an improved current tracking accuracy. A moving average filter (MAF) is implemented using a field programmable gate array (FPGA) for signal pre-processing to eliminate the switching ripple contamination. An adaptive linear neural network (ADALINE) is used for individual harmonic estimation to achieve selective compensation purpose. The effectiveness and validity of the devised control algorithm are confirmed by extensive simulation and experimental results.

• Journal of Electrical Engineering and Technology
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• 제8권4호
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• pp.863-871
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• 2013

Control of pitch angle of turbine blades is among the controlling methods in the wind turbines; this measure is taken for managing mechanical power generated by wind turbine in different wind velocities. Taking into account the high significance of the power generated by wind turbine and due to the fact that better performance of pitch angle is followed by better quality of turbine-generated power, it is therefore crucially important to optimize the performance of this controller. In the current paper, a PI controller is primarily used to control the pitch angle, and then another controller is designed and replaces PI controller through applying a new strategy i.e. alternating two ADALINE neural networks. According to simulation results, performance of controlling system improves in terms of response speed, response ripple, and ultimately, steady tracing error. The highly significant feature of the proposed intelligent controller is the considerable stability against variations of wind velocity and system parameters.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2020년도 전력전자학술대회
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• pp.282-283
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• 2020

3상 컨버터에서 스위치의 개방고장이 발생한 경우 고장 전류에 직류 및 고조파 성분이 발생할 수 있으며, 보호회로에 의한 고장 감지가 어려우므로 주변 기기에 2차 고장이 발생할 수 있다. 단일 및 이중 스위치 개방고장의 경우 21가지 고장 모드가 존재한다. 본 논문에서는 이러한 고장 모드를 진단하기 위해 정지 좌표계 d-q축 전류의 직류 및 고조파 성분을 활용하는 two-step 구조의 ANN(Artificial Neural Network)을 제안한다. 고장 시에 발생된 직류 및 고조파 성분 전류는 ADALINE(Adaptive-Linear Neuron)을 통해 얻는다. 고장 진단의 첫 번째 단계에서는 직류 성분을 기반으로 ANN을 이용하여 고장모드를 6개 영역으로 분류한다. 두 번째 단계에서는 6개의 각 영역에서 직류 성분과 전류의 THD(Total Harmonics Distortion)를 기반으로 ANN을 이용하여 개방고장이 발생한 스위치를 진단한다. 제안된 Two-step 방법으로 고장을 진단하므로써 간단한 구조로 ANN의 설계가 가능하다. 3.7kW급 3상 PWM 컨버터로 실험을 통해 제안된 방법의 효용성을 검증하였다.