• Journal of Power Electronics
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• 제13권6호
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• pp.991-999
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• 2013

In this paper, a fast transient buck converter using hysteresis PWM control is presented. The proposed control method is based on hysteresis control of the capacitor C voltage. This offers a faster transient response to meet the challenges of the power supply requirements for fast dynamic input and load changes. It also provides better stability and solves the compensation problem of the error amplifier in conversional voltage PWM control. Finally, the steady-state and dynamic operation of the proposed control method are analyzed and verified by simulation and experimental results.

• 한국정밀공학회지
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• 제14권5호
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• pp.157-163
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• 1997

In the case of direct connecting the nut of ballscrew to guide table, machining error and misalignment of ballscrew largely affect to the motional accuracy of guideway. For decreasing these influences, two type of floating couplings: leaf spring type and hybrid type which releases the table from nut of ballscrew except feed and rotational direction is proposed in this study. In order to verify practical availability of the proposed floating couplings, motional accuracy, dynamic characteristics and micro step response of hydrostatic guideway, mounted with each type of couplings are tested. The conventional fixed type coupling is also tested as the reference in characteristics. From the results of experiments, it is proved that the hybrid type coupling is superior to other couplings and is available to high precision feeding system with ballscrew.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1994년도 추계학술대회 논문집
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• pp.266-272
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• 1994

An essential ingredient in precision machining is a positioning system that responds quickly and precisely to very small input signal. In this paper, two different positioning systems were presented fot the precision positioning control. The one is a friction drive system, the other is a ballscrew system. The friction drive system was composed of an air sliding guide and a friction drive. The ballscrew system was made of a ballscrew and a linear guide. Nonlinear behaviors of the given systems tend to make the system inaccurate. The paper looked at the phenomena that has caused the positioning error. These apparently nonlinear phenomena can be attributed mainly to the presence of the nonlinear friction and slip effect plus the dynamic change from the microdynamic to the macrodynamic and form the macrodynamic to the microdynamic. For the control of the positioning system, the control algorithm based on a neural network is suggested. The FEL(Feedback Error Learning) controller can learn the inverse dynamics of a nonlinear system by using the neural network controller, and stabilize the system by a linear controller. In the experiment, PTP control is implemented withen the maximum error of 0.05 .mu.m ~0.1 .mu. m when i .mu.m step reference input is applied and that of maximum 1 .mu. m when 100 .mu.m step reference input is given. Sinusoidal inputs with the amplitude of 1 .mu.m and 100 .mu. m are used for the tracking control of the positioning system. Experimental results of the proposed algorithm are shown to be superior to those of conventional PD controls.

• Journal of Welding and Joining
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• 제14권6호
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• pp.131-139
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• 1996

The response of the arc sensor using the welding current and/or welding voltage as its outputs has been obtained by the analysis and/or experiments of the static characteristics of arc sensor. But in order to improve the reliability of arc sensor, it is necessary to know its dynamic characteristics. So in this paper, it is presented the dynamic model of arc sensor including the power source, arc voltage, electrode burnoff rate, and wire feed rate. A numerical simulation of the dynamic model of arc sensor was implemented, computing the welding current with input of CTWD. The results of computer simulations and experiments of $CO_2$arc welding showed that a linear relationship between weaving center - weld line distance and current area difference was established. Additionally, a real-time weld seam tracking system interfaced with industrial welding robot was constructed, the result of the weld seam tracking experiment for weld line with an initial offset error of 5$^{\circ}$was good.

• Korean Chemical Engineering Research
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• 제61권2호
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• pp.217-225
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• 2023

화장품, 비료 등 다양한 분야에서 사용되는 2,3-butanediol (2,3-BDO) 는 고부가가치 물질로 그 수요가 점차 증가하고 있다. 미생물의 발효로부터 생산된 2,3-BDO는 발효의 부산물을 포함하고 있을 뿐만 아니라 발효 조건에 따라 피드 조성의 변동이 심하여 생산물의 목표 순도에 도달하기 위한 분리 공정의 효율적인 운전이 어렵다. 따라서 본 연구에서는 바이오 기반 2,3-BDO 증류 공정의 동적 최적화를 통해 피드의 농도가 변화할 때 하단 생산물의 2,3-BDO 농도를 99 wt% 이상으로 제어할 수 있는 최적의 제어 경로를 탐색하였다. 정상 및 동적 상태 공정 모사와 Proportional integral (PI) 제어기 설계 후 동적 최적화를 차례로 수행하였다. 그 결과 하단 생산물의 2,3-BDO 농도와 설정점 사이의 오차가 75.2% 감소하였다.

• Membrane and Water Treatment
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• 제9권2호
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• pp.115-121
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• 2018

Artificial neural network (ANN) simulation is used to predict the dynamic change of permeate flux during wheat starch industry wastewater microfiltration with and without static turbulence promoter. The experimental program spans range of a sedimentation times from 2 to 4 h, for feed flow rates 50 to 150 L/h, at transmembrane pressures covering the range of $1{\times}10^5$ to $3{\times}10^5Pa$. ANN predictions of the wastewater microfiltration are compared with experimental results obtained using two different set of microfiltration experiments, with and without static turbulence promoter. The effects of the training algorithm, neural network architectures on the ANN performance are discussed. For the most of the cases considered, the ANN proved to be an adequate interpolation tool, where an excellent prediction was obtained using automated Bayesian regularization as training algorithm. The optimal ANN architecture was determined as 4-10-1 with hyperbolic tangent sigmoid transfer function transfer function for hidden and output layers. The error distributions of data revealed that experimental results are in very good agreement with computed ones with only 2% data points had absolute relative error greater than 20% for the microfiltration without static turbulence promoter whereas for the microfiltration with static turbulence promoter it was 1%. The contribution of filtration time variable to flux values provided by ANNs was determined in an important level at the range of 52-66% due to increased membrane fouling by the time. In the case of microfiltration with static turbulence promoter, relative importance of transmembrane pressure and feed flow rate increased for about 30%.

• 제어로봇시스템학회논문지
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• 제22권5호
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• pp.320-325
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• 2016

This paper presents a robust finite-time sliding mode control (SMC) scheme for unknown disturbance and unmodeled nonlinear friction and dynamics in the robotic manipulator. A finite-time SMC (FSMC) surface and finite-time sliding mode controller are constructed to obtain faster error convergence than the conventional infinite-time based SMC. By adding prescribed constraint control term to a finite-time SMC to compensate for unknown disturbance and uncertainties, a robust control scheme can be designed as well as faster convergence control. In addition, simpler controller structure is built by using feed-forwarding upper bound coefficients of each manipulator dynamic parameters instead of model-based control or adaptive observer to estimate unknown manipulator parameters. Simulation and experimental evaluations highlight the efficacy of the proposed control scheme for an articulated robotic manipulator.

• 한국공작기계학회논문집
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• 제16권5호
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• pp.108-114
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• 2007

A new cutting force estimating approach and machining state monitoring examples are presented which uses a cylindrical displacement sensor built into the spindle. To identify the tool-spindle system dynamics with frequency up to 2 kHz, a home-built electro-magnetic exciter is used. The result is used to build an algorithm to extract the dynamic cutting force signal from the spindle error motion; because the built-in spindle sensor signal contains both spindle-tool dynamics and tool-workpiece interactions. This sensor is very sensitive and can measure broadband signal without affecting the system dynamics. The main characteristic is that it is designed so that the measurement is irrelevant to the geometric errors by covering the entire circumferential area between the target and sensor. It is also very simple to be installed. Usually the spindle front cover part is copied and replaced with a new one with this sensor added. It gives valuable information about the operating condition of the spindle at any time. It can be used to monitor cutting force and chatter vibration, to predict roughness and to compensate the form error by overriding spindle speed or feed rate. This approach is particularly useful in monitoring a high speed machining process.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1996년도 춘계학술대회 논문집
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• pp.610-614
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• 1996

As the run out error and misalignment of ball screw connected directly to guide table largely affect the motion accuracy of guideway, floating coupling that releases the table from screw nut except feed and rotational direction is needed todecrease its influences. The purpose of this study is to propose a practical model floating coupling of ball serew for high precision feeding system. The straightness, dynanic characteristics and micro step response of hydrostatic guideway, mounted with three types of coupling fixed type, leaf spring type and hydrostatic type, are tested and compared. From the resuts of experiments, it is proved that a hydrostatic type floating coupling is superior to other couplings and is available to high precision feeding system with ball screw.

• 한국정밀공학회지
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• 제16권11호
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• pp.158-165
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• 1999

It has been established that a crack has an important effect on the dynamic behavior of a structure. This effect depends mainly on the location and depth of the crack. To identify the location and depth of a crack in a structure, a method is presented in this paper which uses hybrid neuro-genetic technique. Feed-forward multilayer neural networks trained by back-propagation are used to learn the input)the location and dept of a crack)-output(the structural eigenfrequencies) relation of the structural system. With this neural network and genetic algorithm, it is possible to formulate the inverse problem. Neural network training algorithm is the back propagation algorithm with the momentum method to attain stable convergence in the training process and with the adaptive learning rate method to speed up convergence. Finally, genetic algorithm is used to fine the minimum square error.