• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.581-586
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• 2003

This paper presents control designs using neural networks (NN) for a XY positioning table. The proposed neurocontroller is composed of an outer PD tracking loop for stabilization of the fast flexible-mode dynamics and an NN inner loop used to compensate for the system nonlinearities. A tuning algorithm is given for the NN weights, so that the NN compensation scheme becomes adaptive, guaranteeing small tracking errors and bounded weight estimates. Formal nonlinear stability proofs are given to show that the tracking error is small. The proposed neuro-controller is implemented and tested on an IBM PC-based XY positioning table, and is applicable to many precision XY tables. The algorithm, simulation, and experimental results are described. The experimental results are shown to be superior to those of conventional control.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.41 no.2
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• pp.17-28
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• 2004

A deadzone compensator is designed for a XY positioning table using fuzzy logic. The classification property of fuzzy logic systems makes them a natural candidate for the rejection of errors induced by the deadzone, which has regions in which it behaves differently. A tuning algorithm is given for the fuzzy logic parameters, so that the deadzone compensation scheme becomes adaptive, guaranteeing small tracking errors and bounded parameter estimates. Formal nonlinear stability proofs are given to show that the tracking error is small. The fuzzy logic deadzone compensator is implemented on a XY positioning table to show its efficacy.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.3
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• pp.375-382
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• 2004

This paper presents control designs using neural networks (NN) for a XY positioning table. The proposed neuro-controller is composed of an outer PD tracking loop for stabilization of the fast flexible-mode dynamics and an NN inner loop used to compensate for the system nonlinearities. A tuning algorithm is given for the NN weights, so that the NN compensation scheme becomes adaptive, guaranteeing small tracking errors and bounded weight estimates. Formal nonlinear stability proofs are given to show that the tracking error is small. The proposed neuro-controller is implemented and tested on an IBM PC-based XY positioning table, and is applicable to many precision XY tables. The algorithm, simulation, and experimental results are described. The experimental results are shown to be superior to those of conventional control.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.7
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• pp.62-70
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• 1999

To achieve precision positioning, working area is required within $5mm{\times}5mm$ and positioning error is allowed within minimum ${\pm}4{\mu}m$. As a general three-layered table takes working range from several centimeters and a few tens of centimeters, it has disadvantages compared with precision positioning table, such as larger working range and rough accuracy. In this paper we design and implement a parallel $XY{\theta}$ table with three linear actuators, where one is on the horizontal direction and the others on the vertical direction on behalf of a degree of $XY{\theta}$ freedom. Finally, the experimental results of precision positioning is showed by using new image processing algorithms with two CCD cameras.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.2
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• pp.113-119
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• 2002

An FLNN-based neural network controller is applied to precise positioning of XY table with friction as the extension study of [11]. The neural network identifies the frictional farces of the table. Its weight adaptation rule, named the reinforcement adaptive learning rule, is derived from the Lyapunov stability theory. The experimental results with 2-DOF XY table verify the effectiveness of the proposed control scheme. It is also expected that the proposed control approach is applicable to a wide class of mechanical systems.

• Journal of the Semiconductor & Display Technology
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• v.12 no.1
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• pp.53-59
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• 2013

Chip mounter which is used to pick chips from the pre-specified position and place them on the target location of PCB is an essential device in semiconductor and LCD industries. Quick and high precision positioning is the key technology needed to increase productivity of chip mounters. As increasing acceleration and deceleration of placing motion, structural vibration induced from inertial reactive force and flexibility of mounter structure becomes a serious problem degrading positioning accuracy. Motivated from these, this paper proposed a new control design algorithm which combines a mounter structure acceleration feedforward compensation and an extended sliding mode control for fine positioning and suppression of structural vibration, simultaneously. The feasibility of the proposed control design was verified along with some simulation results.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.102.4-102
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• 2002

A deadzone compensator is designed for a positioning system using fuzzy logic. The classification property of fuzzy logic systems make them a natural candidate for the rejection of errors induced by the deadzone, which has regions in which it behaves differently. A tuning algorithm is given for the fuzzy logic parameters, so that the deadzone compensation scheme becomes adaptive, guaranteeing small tracking errors and bounded parameter estimates, formal nonlinear stability proofs are given to show that the tracking error is small. The fuzzy logic deadzone compensator is implemented on a positioning system to show its efficacy. 1. Deadzone Compansation 2. XY positioning table 3. Fuzzy Logic 4. Actuator nonlinearity