• Proceedings of the KIPE Conference
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• 2020.08a
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• pp.328-329
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• 2020

The AC/DC converter, which connects the AC grid to the DC grid in the microgrid, is a critical component in power sharing and stable operation. Sometimes the AC/DC converters are connected in parallel to increase the transmission and reception capacity. When connected in parallel, circulating current is generated due to line impedance difference or sensor error. As a result of circulating current, there is deterioration and loss in particular PCS(Power Conversion System). In this paper, we propose droop control with novel adaptive virtual impedance for reducing circulating current. Feasibility of proposed algorithm is verified by PowerSIM simulation.

• Journal of Internet Computing and Services
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• v.24 no.4
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• pp.37-45
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• 2023

Adaptive has gained significant attention in Education Technology (EdTech), with personalized learning experiences becoming increasingly important. Next-generation chatbots, including models like ChatGPT, are emerging in the field of education. These advanced tools show great potential for delivering personalized and adaptive learning experiences. This paper reviews previous research on adaptive learning and the role of chatbots in education. Based on this, the paper explores current and future chatbot technologies to propose a framework for using ChatGPT or similar chatbots in adaptive learning. The framework includes personalized design, targeted resources and feedback, multi-turn dialogue models, reinforcement learning, and fine-tuning. The proposed framework also considers learning attributes such as age, gender, cognitive ability, prior knowledge, pacing, level of questions, interaction strategies, and learner control. However, the proposed framework has yet to be evaluated for its usability or effectiveness in practice, and the applicability of the framework may vary depending on the specific field of study. Through proposing this framework, we hope to encourage learners to more actively leverage current technologies, and likewise, inspire educators to integrate these technologies more proactively into their curricula. Future research should evaluate the proposed framework through actual implementation and explore how it can be adapted to different domains of study to provide a more comprehensive understanding of its potential applications in adaptive learning.

• Proceedings of the KIPE Conference
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• 1999.07a
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• pp.4-9
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• 1999

A new control method for precision robust position control of a brushless DC (BLDC) motor using asymptotically stable adaptive load torque observer is presented in the paper. Precision position control is obtained for the BLDC motor system approximately linearized using the field-orientation method Recently, many of these drive systems use BLDC motors to avoid backlashe. However, the disadvantages of the motor are high cost and complex control because of nonlinear characteristics. Also, the load torque disturbance directly affects the motor shaft. The application of the load torque observer is published in [1] using fixed gain. However, the motor flux linkage is not exactly known for a load torque observer. There is the problem of uncertainty to obtain very high precision position control. Therefore a model reference adaptive observer is considered to overcome the problem of unknown parameter and torque disturbance in this paper. The system stability analysis is carried out using Lyapunov stability theorem. As a result, asymptotically stable observe gain can be obtained without affecting the overall system response. The load disturbance detected by the asymptotically stable adaptive observer is compensated by feedforwarding the equivalent current which gives fast response. The experimenta results are presented in the paper.

• Proceedings of the KIEE Conference
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• 1995.07a
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• pp.391-393
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• 1995

An improved predictive control technique using adaptive load estimation is proposed. The conventional predictive control technique has not concerned load variations and system parameters. Thus control performances are undesirable such as large current ripples and offset. In this paper the proposed controller employing a simple adaptive algorithm to estimate load is expected to be useful to overcome the problems of conventinal predictive controller.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.230-230
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• 2000

This paper describes a model reference adaptive system(MRAS) for speed control of vector-controlled induction motor without a speed sensor. The proposed approach is based on observing the instantaneous torque. The real torque is calculated by sensing stator current and estimated torque is calculated by stator current that is calculated by using estimated rotor speed. The speed estimation error is linearly proportional to error between real torque and estimated torque. The proposed feedback loop has linear component. Furthermore proposed method is robust to parameters variation. The effectiveness is verified by equation and simulation

• Proceedings of the KIEE Conference
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• 2006.07d
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• pp.1962-1964
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• 2006

In this paper, the dynamic models of SOFC are suggested. It consists of electrochemical model, thermal model, voltage equation and several loss equations. Control problems on tracking steady voltage by air flow is discussed and an adaptive controller is designed to withstand to the variation of stack current. Simulation is done to prove the solution of control algorithms.

• Proceedings of the KIEE Conference
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• 2007.11c
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• pp.51-54
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• 2007

In this paper, the dynamic models of a SOFC are rearranged. It consists of electrochemical model, thermal model, voltage equation and several loss equations. Experiment results of the real SOFC system are shown to evaluate the steady voltage characteristics. Control problems on tracking steady voltage by air flow is discussed and an adaptive controller is designed to withstand to the variation of stack current. Simulation is done to prove the solution of control algorithms.

• Journal of Auto-vehicle Safety Association
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• v.13 no.4
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• pp.129-143
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• 2021

This paper proposes actuator fault detection and adaptive fault-tolerant control algorithms using performance index and human-like learning for longitudinal autonomous vehicles. Conventional longitudinal controller for autonomous driving consists of supervisory, upper level and lower level controllers. In this paper, feedback control law and PID control algorithm have been used for upper level and lower level controllers, respectively. For actuator fault-tolerant control, adaptive rule has been designed using the gradient descent method with estimated coefficients. In order to adjust the control parameter used for determination of adaptation gain, human-like learning algorithm has been designed based on perceptron learning method using control errors and control parameter. It is designed that the learning algorithm determines current control parameter by saving it in memory and updating based on the cost function-based gradient descent method. Based on the updated control parameter, the longitudinal acceleration has been computed adaptively using feedback law for actuator fault-tolerant control. The finite window-based performance index has been designed for detection and evaluation of actuator performance degradation using control error.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.3
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• pp.110-114
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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. In this paper maximum torque control of IPMSM drive using artificial intelligent(AI) controller is proposed. 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-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 AI controller. This paper is proposed speed control of IPMSM using adaptive learning mechanism fuzzy neural network(ALM-FNN) and estimation of speed using artificial neural network(ANN) controller. 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 ALM-FNN and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper is proposed the experimental results to verify the effectiveness of AI controller.

• 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.