• The Transactions of the Korean Institute of Power Electronics
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• v.7 no.5
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• pp.413-418
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• 2002

This paper proposed a feed-forward adaptive position controller that is robust for variable Inertia. The control system consists of PI Position controller, feed-forward and model reference adaptive control. A parameter g(t) of the feed-forward adaptive position controller is adapted by using both the reference model speed and position error. So it improves the transient response and reduces the settling time. And normalization function Is used to make linear adaptation time. The validity of the feed-forward adaptive controller is confirmed by simulation results.

• Journal of Advanced Marine Engineering and Technology
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• v.28 no.2
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• pp.228-236
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• 2004

In this paper. we present a neuro-fuzzy controller which unifies both fuzzy logic and multi-layered feed forward neural networks. Fuzzy logic provides a means for converting linguistic control knowledge into control actions. On the other hand. feed forward neural networks provide salient features. such as learning and parallelism. In the proposed neuro-fuzzy controller. the parameters of membership functions in the antecedent part of fuzzy inference rules are identified by using the error back propagation algorithm as a learning rule. while the coefficients of the linear combination of input variables in the consequent part are determined by using the least square estimation method. Finally. the effectiveness of the proposed controller is verified through computer simulation for an inverted pole system.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1994.10a
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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 the Korean Society of Manufacturing Technology Engineers
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• v.20 no.6
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• pp.703-713
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• 2011

In this study, to minimize trial and error in the design and manufacturing processes of a high-precision large-surface micro-grooving machine which is able to fabricate the molds for 42 inch LCD light guide panels, the effects of the structural deformation of the micro-grooving machine according to the positions of the X-axis, Y-axis and Z-axis feed systems were examined on the tool tip displacement errors associated with the machining accuracy. The virtual prototype (finite element model) of the micro-grooving machine was constructed to include the joint stiffnesses of the hydrostatic bearings, hydrostatic guideways and linear motors, and then the tool tip displacement errors were measured from the virtual prototype. Especially, to establish the prediction model of the tool tip displacement errors, which was constructed using the positions of the X-axis, Y-axis and Z-axis feed systems as independent variables, the response surface method based on the central composite design was introduced. The reliability of the prediction model was verified by the fact that the tool tip displacement errors obtained from the prediction model coincided well those measured from the virtual prototype. And the causes of the tool tip displacement errors were identified through the analysis of interactions between the positions of the X-axis, Y-axis and Z-axis feed systems.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2001.10a
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• pp.99-104
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• 2001

The neural network method has been introduced to design a controller for linear motor feed system and system operation status was monitored. It is most difficult to achieve controller gain tuning because of the information limit. Regardless of the system structure, conventional control gain could be adjusted minimizing the resulting error for both position and velocity using the proposed method. Slight performance deterioration was observed at the small value of training epoch. Different controller performance for position was observed with respect changed sampling time. Actuated system performance was monitored using neural network signal processing and operational status was predicted with the rate of 80% approximately.

• Journal of the Korean Society for Precision Engineering
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• v.23 no.1 s.178
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• pp.113-120
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• 2006

This paper proposed the control algorithm fur aspheric surface grinding and was verified by the experiment. The functions of the algorithm were simultaneous control of the position and interpolation of the aspheric curve. The non-linear formula of the tool position was derived from the aspheric equations and the shape of the tool. The function was partitioned by an certain interval and the control parameters were calculated at each control section. The movement in a session was interpolated with acceleration and velocity. The position error was feed-backed by rotary encorder. The concept of feedback algorithm was correcting position error by increasing or decreasing the speed. In the experiment, two-axis machine was controlled to track the aspheric surface by the proposed algorithm. The effect of the control and process parameters was monitored. The result showed that the maximum tracking error was under sub-micro level for the concave and convex surfaces.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.40 no.1
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• pp.24-36
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• 2003

The digital mobile communication will be developed till getting multimedia service in anyone, any where, any time. Theses requiring items are going to be come true via IMT-2000 system. Transmitting signal bandwidth of IMT-2000 system is 3 times as large as IS-95 system. That is mean peak to average of signal is higher than IS-95A system. So we have to design it carefully not to effect in adjacent channel. HPA(High Power Amplifier) located in the end point of system is operated in 1-㏈ compression point(Pl㏈), then it generates 3rd and 5th inter modulation signals. Theses signals affect at adjacent channel and RF signal is distorted by compressed signal which is operated near by Pl㏈ point. Then the most important design factor is how we make HPA having high linearity. Feedback, Pre-distorter and Feed-forward methods are presented to solve theses problems. Feed-forward of these methods is having excellent improving capacity, but composed with complex structure. Generally, Linearity and Efficiency in power amplifier operate in the contrary, then it is difficult for us to find optimal operating point. In this paper we applied algorithm which searches optimal point of linear characteristics, which is key in Power Amplifier, using minimum current point of error amplifier in 1st loop. And we made 2nd loop compose with new structure. We confirmed fabricated LPA is operated by having high linearity and minimum current condition with ACPR of -26 ㏈m max. @ 30㎑ BW in 3.515㎒ and ACLR of 48 ㏈c max@${\pm}$㎒ from 1W to 40W.

• The Bulletin of The Korean Astronomical Society
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• v.41 no.2
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• pp.66.2-66.2
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• 2016

KASI and NAOJ are making collaborating efforts to implement faster mapping capability into the new 275-500 GHz Atacama Submillimeter Telescope Experiment focal plane array (FPA). Feed horn antenna is one of critical parts of the FPA. Required fractional bandwidth is almost 60 % while that of traditional conical horn is less than 50 %. Therefore, to achieve this wideband performance, we adopted a horn of which the corrugation depths have a longitudinal profile. A profiled horn has features not only of wide bandwidth but also of shorter length compared to a linear-tapered corrugated horn, and lower cost fabrication with less error can be feasible. In our design process the flare region is represented by a cubic splined curve with several parameters. Parameters of the flare region and each dimension of the throat region are optimized by a differential evolution algorithm to keep >20 dB return loss and >30 dB maximum cross-polarization level over the operation bandwidth. To evaluate RF performance of the horn generated by the optimizer, we used a commercial mode matching software, WASP-NET. Also, Gaussian beam (GB) masks to far fields were applied to give better GB behavior over frequencies. The optimized design shows >23 dB return loss and >33 dB maximum cross-polarization level over the whole band. Gaussicity of the horn is over 96.6 %. The length of the horn is 12.5 mm which is just 57 % of the ALMA band 8 feed horn (21.96 mm).

• Transactions of the Korean Society of Mechanical Engineers
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• v.16 no.9
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• pp.1669-1680
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• 1992

This paper deals with the prediction of cutting force and tool deflection and it's application in the flexible ball end milling process. Machining accuracy is determined by the static stiffness of tool system and the instantaneous cutting force. The static stiffness of tool system consists of the stiffness of holer and the stiffness of ball end mill. The stiffness of holder was obtained from the experimental result, and the stiffness of ball end mill with two flutes was theoretically analyzed by the finite elements method. In cutting process, the instantaneous cutting force is dependent upon the instantaneous feed and pick feed(radial depth of cut) which are varied by tool deflection. For the calculation of cutting force and deflection of ball end mill, iteration method is used with the linear interpolation to the data of cutting force obtained from rigid ball end mill and the data of tool deflection. In this paper, a for enhancing accuracy is discussed. And the selection of helix angle for minimizing machining error is also discussed.

• Journal of the Korean Society for Precision Engineering
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• v.24 no.4 s.193
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• pp.93-101
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• 2007

Aluminum alloy which is one of the light materials has been tried to apply to light weight vehicle body. In order to do that, welding technology is very important. In case of the aluminum laser welding, the strength of welded part is reduced due to porosity, underfill, and magnesium loss. To overcome these problems, laser welding of aluminum with filler wire was suggested. In this study, experiment about laser welding of AA5182 aluminum alloy with AA5356 filler wire was performed according to process parameters such as laser power, welding speed and wire feed rate. The tensile strength was measured to find the weldability of laser welding with filler wire. The models to estimate tensile strength were suggested using three regression models and one neural network model. For regression models, one was the multiple linear regression model, another was the second order polynomial regression model, and the other was the multiple nonlinear regression model. Neural network model with 2 hidden layers which had 5 and 3 nodes respectively was investigated to find the most suitable model for the system. Estimation performance was evaluated for each model using the average error rate. Among the three regression models, the second order polynomial regression model had the best estimation performance. For all models, neural network model has the best estimation performance.