• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1233-1236
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• 1996

Attitude Control System for a Hydrofoil type catamaran in wave is designed using a Decentralized Adaptive Control technique which is announced already by authors. This automatic attitude control system is designed for its good seaworthiness and for robustness on the variation of center of gravity. The performance is compared with a PID controller and the results show that the Decentralized Adaptive controller has better stability on the variation of the center of gravity.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.32A no.8
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• pp.11-18
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• 1995

This paper proposes a nonlinear adaptive equalizer which is based on fuzzy rules and fuzzy inference of several affine mapping for the received channel data. The proposed nolonlinear adaptive equalizers with the significantly lower computational complexity. Also it can be applied to the on-line adaptation environments owing to its fast convergence characteristics and the lower computational load. When using the decision feedback vectors, this equaalizer can be easily realized in the form of the DFE structure with out the requirement for the perfect channel knowledge as in the case of the fuzzy adaptive filter.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.349-349
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• 2000

A Clustering Adaptive Fuzzy Logic Controller(CAFLC) is applied to the torque control of a brushless do motor drive. Objective of this system includes elimination of torque ripple due to cogging at low speeds under loads. The CAFLC implemented has advantages of computational simplicity, and self-tuning characteristics. Simulation results showed that the torque ripple and dynamic response of the system using a CAFLC were superior to the model reference adaptive controlled system.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.994-997
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• 2003

This Paper presents a study of the performance of a DC servo motor with a model reference adaptive fuzzy speed controller (MRAFSC) in the presences of load disturbances. MRAFSC comprised inner feedback loop consisting of the fuzzy logic controller (FLC) and plant, and outer loop consisting of an adaptation mechanism which is designed for tuning a control rule of the FLC. Experimental results show the good performance in the DC servo motor system with the proposed adaptive fuzzy controller.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.48.1-48
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• 2001

In this paper, we present a global adaptive output feedback control scheme for a class of uncertain nonlinear systems to which adaptive observer backstepping method may not be applicable directly. The allowed output feedback structure includes quadratic and multiplicative dependency of unmeasured states. Our novel design technique employs a change of coordinates and adaptive backstepping. With these proposed tools, we can remove linear and quadratic dependence on the unmeasured states in the state equation. Also, the multiplication of the two unmeasured states can be eliminated ...

• Journal of Digital Convergence
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• v.19 no.10
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• pp.39-48
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• 2021

This study aimed to analyze the level of professors' understanding and perception of adaptive learning and proposed how college can implement successful adaptive learning in college classes. For research purposes, online survey was conducted by 162 professors of A university in capital region. As a result, professors seemed to feel pressure to provide students personalized feedback and gave concerned that students don't study enough in advance before participating in class. It was also found that professors realized that they have low level of understanding about adaptive learning, while they revealed intention to make use of adaptive learning in their class. They also answered that adaptive learning system is the most helpful support for encouraging professors to apply adaptive learning in real class. We proposed what is required to encourage professor to implement adaptive learning in their class.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.38 no.8
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• pp.1-12
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• 2001

The adaptive learning circuit is designed on the basis of modeling of MFSFET (Metal-Ferroelectric-Semiconductor FET) and the numerical results are analyzed. The output frequency of the adaptive learning circuit is inversely proportional to the source-drain resistance of MFSFET and the capacitance of the circuit. The saturated drain current with input pulse number is analogous to the ferroelectric polarization reversal. It indicates that the ferroelectric polarization plays an important role in the drain current control of MFSFET. The output frequency modulation of the adaptive learning circuit is investigated by analyzing the source-drain resistance of MFSFET as functions of input pulse numbers in the adaptive learning circuit and the dimensionality factor of the ferroelectric thin film. From the results, the frequency modulation characteristic of the adaptive learning circuit are confirmed. In other words, adaptive learning characteristics which means a gradual frequency change of output pulse with the progress of input pulse are confirmed. Consequently it is shown that our circuit can be used effectively in the neuron synapses of nueral networks.

• Journal of the Ergonomics Society of Korea
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• v.24 no.4
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• pp.73-78
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• 2005

As the number of functions in a menu increases, users have more difficulties in finding a desired function. Previous studies have shown that some functions are selected more frequently than others, and have suggested adaptive menus that support the selection of frequently used functions. Interestingly, studies on an adaptive toolbar are not easy to find as opposed to many studies on adaptive menus. This study suggested an adaptive toolbar (AT) that supported function selection, and conducted a usability test. Five or ten functions were presented in the AT according to the frequency of use or recency of use. A total of sixteen males in their twenties participated in the test. They freely selected functions from the menu or from the AT, and their pattern of selecting functions was analyzed. The results showed that the AT was more frequently used than the menu as time passed. The AT based on the recency of use showed more effective performance than that based on the frequency of use. In addition, keeping ten functions was better than five functions in terms of both performance and preference.

• Journal of Power Electronics
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• v.16 no.3
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• pp.1067-1075
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• 2016

This paper presents an adaptive observer-based approach to estimate voltage parameters, including frequency, amplitude, and phase angle, for single-phase power systems. In contrast to most existing estimation methods of grid voltage parameters, in this study, grid voltage is treated as a dynamic system related to an unknown grid frequency. Based on adaptive observer theory, a full-order adaptive observer is proposed to estimate voltage parameters. A Lyapunov function-based argument is employed to ensure that the proposed estimation method of voltage parameters has zero steady-state error, even when frequency varies or phase angle jumps significantly. Meanwhile, a reduced-order adaptive observer is designed as the simplified version of the proposed full-order observer. Compared with the frequency-adaptive virtual flux estimation, the proposed adaptive observers exhibit better dynamic response to track the actual grid voltage frequency, amplitude, and phase angle. Simulations and experiments have been conducted to validate the effectiveness of the proposed observers.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2006.05a
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• pp.309-314
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• 2006

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. This paper proposes maximum torque control of IPMSM drive using adaptive learning fuzzy neural network and artificial neural network. This control method is applicable over the entire speed range which considered the limits of the inverter's current md voltage rated value. For each control mode, a condition that determines the optimal d-axis current $i_d$ for maximum torque operation is derived. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using adaptive teaming fuzzy neural network and artificial neural network. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also, this paper proposes speed control of IPMSM using adaptive teaming fuzzy neural network and estimation of speed using artificial neural network. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The proposed control algorithm is applied to IPMSM drive system controlled adaptive teaming fuzzy neural network and artificial neural network, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper proposes the analysis results to verify the effectiveness of the adaptive teaming fuzzy neural network and artificial neural network.