• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.27 no.4
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• pp.48-53
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• 2013

In general, the LLC resonant topology consists of three stages as; square wave generator, resonant network, and rectifier network. LLC resonant converter has the time slowly varying parameters. However, the power LEDs as the load of LLC converter can be regarded as fast time varying parameters. In this paper, the mathematical model of half-bridge resonant converter including with the power LEDs is introduced for the current controller design model. Using this controller design model, the parameter adaptive output feedback controller will be designed to control the power LEDs current. In order to show the validities of the proposed model, the parameter adaptive output feedback controller, the experimental investigation will be presented.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.39 no.4
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• pp.93-103
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• 2002

A new current mirror for high-accuracy current-mode signal processing and integrated circuit design was proposed. The current mirror adopts the technique of an adaptive feedback to reduce the input impedance and the output stage of regulated cascode current mirror to increase the output impedance. Simulation results show that the current mirror has input impedance of 0.9Ω, the output impedance of 415 MΩ, and current gain of 0.96 at the supply voltage Vcc=5V. The power dissipation is 1.5㎽. In order to certify the applicability of the proposed current mirror, a voltage-to-current converter using the current mirror is designed. Simulation results show that the converter has good agreement with theoretical equation and has three times better conversion characteristics when compared with voltage-to-current converter using Wilson current mirror.

• JSTS:Journal of Semiconductor Technology and Science
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• v.15 no.3
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• pp.326-333
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• 2015

This paper reports an adaptive positive feedback bias control technique for operational transconductance amplifiers to adjust the bias current based on the output current monitored by a current replica circuit. This technique enables operational transconductance amplifiers to quickly adapt their power consumption to various analog workloads when they are configured with negative feedback. To prove the concept, a test voltage follower is fabricated in $0.5-{\mu}m$ CMOS technology. Measurement result shows that the power consumption of the test voltage follower is approximately linearly proportional to the load capacitance, the signal frequency, and the signal amplitude for sinusoidal inputs as well as square pulses.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.840-843
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• 1996

This paper presents an adaptive cutting force controller for milling process, which can be attached to most commercial CNC machining centers in a practical way. The cutting forces of X,Y and Z axes measured indirectly from the use of currents drawn by AC feed-drive servo motors. A typical model for the feed-drive control system of a horizontal machining center is developed to analyze cutting force measurement from the drive motor. The pulsating milling forces can be measured indirectly within the bandwidth of the current feedback control loop of the feed-drive system. It is shown that indirectly measured cutting force signals can be used in the adaptive controller for cutting force regulation. The robust controller structure is adopted in the whole adaptive control scheme. The conditions under which the whole scheme is globally convergent and stable are presented. The suggested control scheme has been implemented into a commercial machining center, and a series of cutting experiments on end milling and face milling processes are performed. The adaptive controller reveals reliable cutting force regulating capability under various cutting conditions.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.673-676
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• 2015

Digital Pre-Distortion (DPD) is a linearization technique for nonlinear power amplifiers (PAs) by implementing inverse function of the PA at baseband digital stage. To obtain proper DPD parameters, a feedback path is required to convert the PA output to a baseband signal, and a memory is also needed to store the feedback signals. DPD parameters are usually found by an adaptive algorithm from the feedback samples. However, for the adaptive algorithm to converge to a reliable solution, long feedback samples are required, which increases convergence time and hardware complexity. In this paper, we propose a DPD technique that requires relatively short feedback samples. From the observation that the convergence time of the adaptive algorithm highly depends on the initial condition, this paper iteratively utilizes the feedback samples while keeping and using the converged DPD parameters at the former iteration as the initial condition at the current iteration. Computer simulation results show that the proposed method performs better than the conventional technique while the former requires much shorter feedback samples than the latter.

• Journal of Communications and Networks
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• v.6 no.2
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• pp.133-146
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• 2004

Recently, many active queue management (AQM) algorithms have been proposed to address the performance degradation. of end-to-end congestion control under tail-drop (TD) queue management at Internet routers. However, these AQM algorithms show performance improvement only for limited network environments, and are insensitive to dynamically changing network situations. In this paper, we propose an adaptive queue management algorithm, called PID-controller, that uses proportional-integral-derivative (PID) feedback control to remedy these weak-Dalles of existing AQM proposals. The PID-controller is able to detect and control congestion adaptively and proactively to dynamically changing network environments using incipient as well as current congestion indications. A simulation study over a wide range of IP traffic conditions shows that PID-controller outperforms other AQM algorithms such as Random Early Detection (RED) [3] and Proportional-Integral (PI) controller [9] in terms of queue length dynamics, packet loss rates, and link utilization.

• 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.

• 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 KIEE Conference
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• 1995.07a
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• pp.334-336
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• 1995

An adaptive feedback linearization technique of a PM synchronous motor with specified output dynamic performance is proposed. The adaptive parameter estimation is achieved by a model reference adaptive technique where the stator resistance and flux linkage can be estimated with the current dynamic model and the state observer. Using these estimated parameters, the linearizing control inputs are calculated and a nonlinear coupled model of a PM synchronous motor is input-output linearized. The resultant model has the load torque disturbance. To get ti perfect decoupled model, the load torque is estimated. The adaptation laws are derived by the hyperstability theory and positivity concept. The robustness of the proposed control scheme will be proven through the computer simulations.

• Journal of Power Electronics
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• v.14 no.4
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• pp.704-711
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• 2014

This paper proposes a combined fuzzy adaptive sliding-mode voltage controller (FASVC) for a three-phase UPS inverter. The proposed FASVC encapsulates two control terms: a fuzzy adaptive compensation control term, which solves the problem of parameter uncertainties, and a sliding-mode feedback control term, which stabilizes the error dynamics of the system. To extract precise load current information, the proposed method uses a conventional load current observer instead of current sensors. In addition, the stability of the proposed control scheme is fully guaranteed by using the Lyapunov stability theory. It is shown that the proposed FASVC can attain excellent voltage regulation features such as a fast dynamic response, low total harmonic distortion (THD), and a small steady-state error under sudden load disturbances, nonlinear loads, and unbalanced loads in the existence of the parameter uncertainties. Finally, experimental results are obtained from a prototype 1 kVA three-phase UPS inverter system via a TMS320F28335 DSP. A comparison of these results with those obtained from a conventional sliding-mode controller (SMC) confirms the superior transient and steady-state performances of the proposed control technique.