• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.2
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• pp.50-59
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• 1998

This paper presents stabilization and adaptive control of flexible single link robot manipulator system by self-recurrent neural networks that is one of the neural networks and is effective in nonlinear control. The architecture of neural networks is a modified model of self-recurrent structure which has a hidden layer. The self-recurrent neural networks can be used to approximate any continuous function to any desired degree of accuracy and the weights are updated by feedback-error learning algorithm. When a flexible manipulator is rotated by a motor through the fixed end, transverse vibration may occur. The motor toroque should be controlled in such a way that the motor rotates by a specified angle, while simultaneously stabilizing vibration of the flexible manipuators so that it is arresed as soon as possible at the end of rotation. Accurate vibration control of lightweight manipulator during the large changes in configuration common to robotic tasks requires dynamic models that describe both the rigid body motions, as well as the flexural vibrations. Therefore, a dynamic models for a flexible single link robot manipulator is derived, and then a comparative analysis was made with linear controller through an simulation and experiment. The results are proesented to illustrate thd advantages and imporved performance of the proposed adaptive control ove the conventional linear controller.

• International Journal of Control, Automation, and Systems
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• v.4 no.6
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• pp.705-713
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• 2006

For the analysis of multiunit extracellular neural signals as multiple spike trains, neural spike sorting is essential. Existing algorithms for the spike sorting have been unsatisfactory when the signal-to-noise ratio(SNR) is low, especially for implementation of fully-automated systems. We present a novel method that shows satisfactory performance even under low SNR, and compare its performance with a recent method based on principal component analysis(PCA) and fuzzy c-means(FCM) clustering algorithm. Our system consists of a spike detector that shows high performance under low SNR, a feature extractor that utilizes projection pursuit based on negentropy maximization, and an unsupervised classifier based on Gaussian mixture model. It is shown that the proposed feature extractor gives better performance compared to the PCA, and the proposed combination of spike detector, feature extraction, and unsupervised classification yields much better performance than the PCA-FCM, in that the realization of fully-automated unsupervised spike sorting becomes more feasible.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1430-1434
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• 2004

Recently, Virtual reality parts is applied in various fields of industry. In this paper we developed basic components for virtual robot control system interfaced with real environment. For this, a real robot with virtual interface module is developed and virtual robot of similar image with real robot is created by putting on 3D graphic texture to the real robot. To build an unknown environment to be linked with virtual environment, we proposed a hough transformation based algorithm. Our proposed algorithm consists of navigation module by using fuzzy engine and map building module. Experiments using a developed robot illustrate the method.

• Proceedings of the KIPE Conference
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• 2011.11a
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• pp.273-274
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• 2011

최근에는 풍속변화에 대응하여 최대전력을 발생할 수 있는 가변속 풍력발전 시스템에 대한 연구가 활발히 진행 중이며 국내의 지형적 조건에 따른 바람의 영향으로 풍력발전 시스템의 MPPT제어가 반드시 필요하다. 종래의 풍력발전 MPPT 제어는 풍속변화에 대한 응답속도 등에 대한 문제점이 나타난다. 따라서 본 논문에서는 파라미터 변동에 대해 강인성 제어가 가능한 FLC(Fuzzy Logic Control)을 기반으로 한 풍력발전시스템의 MPPT 제어를 제안하고, 시뮬레이션 결과를 통하여 타당성을 입증한다.

• Proceedings of the KIEE Conference
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• 2008.04c
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• pp.122-124
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• 2008

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. In order to maximize the efficiency in such applications, this paper proposes the Adaptive-FNIS(Fuzzy Neural Network Inference System). The controllable electrical loss which consists of the copper loss and the iron loss can be minimized by the optimal d-axis current $i_d$. This paper considers the parameter variation about the motor operation. The operating characteristics controlled by efficiency optimization control are examined in detail.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.4 no.1
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• pp.23-28
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• 2004

This paper proposes a sensor-fusion technique where the data sets for the previous moments are properly transformed and fused into the current data sets to enable accurate measurement, such as, distance to an obstacle and location of the service robot itself. In the conventional fusion schemes, the measurement is dependent on the current data sets. As the results, more of sensors are required to measure a certain physical parameter or to improve the accuracy of the measurement. However, in this approach, instead of adding more sensors to the system, the temporal sequence of the data sets are stored and utilized for the measurement improvement. Theoretical basis is illustrated by examples and the effectiveness is proved through the simulations. Finally, the new space and time sensor fusion(STSF) scheme is applied to the control of a mobile robot in an unstructured environment as well as structured environment.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2005.05a
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• pp.461-466
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• 2005

This paper presents a new speed control scheme of the spindle induction motor (IM) using fuzzy-logic control in fold weakening region. The implementation of the proposed FLC-based spindle IM are investigated and compared to those obtained from the conventional PI controller based drive system, we have confirmed good simulation and experimental results at different dynamic operating conditions such as sudden change in command speed, step change, etc.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.812-814
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• 1999

In this paper, we are applied the Genetic Algorithm (GA) to design of fuzzy logic controller (FLC) for a DC Servo-Motor Speed Control. GA is used to design of the membership functions and scaling factor of FLC. To evaluate the performances of the proposed FLC, we make an experiment on FLC for the speed control of an actual DC servo-motor system with nonlinear characteristics. Experimental results show that proposed controller have better performance than those of PD controller.

• Proceedings of the KIEE Conference
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• 2004.07d
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• pp.2304-2306
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• 2004

This paper proposes an Intelligent Speed Estimator in order to realize the speed-sensorless vector control of an induction motor. Intelligent Speed Estimator used Model Reference Adaptive System which has Fuzzy-Neural adaptive mechanism as Speed Estimation method. The Intelligent Speed Estimator estimates the speed of an induction motor with a rotor flux of a reference model and adjustable model in MRAS. The Intelligent Speed Estimator reduces the error of the rotor flux between the voltage flux model and the current flux model using the error and the change of error as input of the Estimator. The computer simulation is executed to verify the propriety and the effectiveness of the proposed speed estimator.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.1144-1145
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• 2008

기존의 MPPT제어의 방식은 태양전지의 출력을 비교하여 최대점을 추종하는 방식으로써 최대전력점을 추종하는데 많은 시간이 필요하다. 따라서 본 논문에서는 새로운 MPPT제어법인 POS(PV Output Senseless) MPPT법에 최대전력추종의 시간이 빠른 퍼지제어를 적용하여, 기존의 MPPT제어의 방식과 퍼지제어와의 최대전력점의 추종시간을 예측할 수 없는 기상조건에서 비교분석하여 제안된 방법의 유효성을 시뮬레이션 결과를 통해 확인하였다.