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Kalman Filter-Based Coarse-to-Fine Control for Display Visual Alignment Systems
SangJoo Kwon,Haemin Jeong,Jaewoong Hwang IEEE 2012 IEEE transactions on automation science and engine Vol.9 No.3
<P>A coarse-to-line two-stage control method is investigated for the display visual alignment systems. The proposed visual servo is with hierarchical loops, where the original line but slow vision loop is necessary for the exact localization of alignment marks while the coarse but fast vision loop of exploiting pruned image data is to compensate for the mask-panel misalignment. The degraded resolution of the reduced images is recovered in terms of the Kalman filter which tracks the mark centroids in near realtime. In order to construct the recursive estimation algorithm, the motion model for the moving alignment marks is determined by solving the forward kinematics of positioning mechanism and the measurements from vision sensors are given by means of the geometric template matching (Kwon and Hwang, “Kinematics, pattern recognition, and motion control of mask-panel alignment system,” Control Eng. Practice, vol. 19, pp. 883-892, 2011). Compared with the conventional alignment methods, this approach enables a fast and line alignment control. Experimental results are followed to validate the proposed control framework. Note to Practitioners-In order to successfully apply the developed alignment control to any display manufacturing equipment, it is necessary to well understand the principle of the geometric template matching (GTM) as an alignment mark specific fast algorithm, the details on which can be consulted in our preceding works (Kwon and Hwang, “Kinematics, pattern recognition, and motion control of mask-panel alignment system,” Control Eng. Practice, vol. 19, pp. 883-892, 2011). The new approach has the goal of updating the pose of an alignment mark as fast as the capturing rate of a frame grabber by utilizing pruned image data but recovering the lost resolution in terms of the Kalman filter. For example, in using a common 30 fps grabber, the reduced image of 320 240 pixels is a proper choice to finish the image processing and Kalman filtering within 30 ms under GTM. The proposed algorithm can be implemented in the current industrial display aligners by modifying the control software so that the reference inputs for the distributed joint servos follow the error compensation trajectory in Fig. 4 with the Kalman filter estimates.</P>
Tilting-Type Balancing Mobile Robot Platform for Enhancing Lateral Stability
SangJoo Kwon,Sangtae Kim,Jaerim Yu IEEE 2015 IEEE/ASME transactions on mechatronics Vol.20 No.3
<P>In this paper, a tilting-type balancing mobile robot platform is investigated for enhancing lateral stability. In addition to pitch, yaw, and straight motion by the conventional two-wheeled inverted pendulum mechanism, it can generate roll and vertical motion by an additional tilting mechanism. The static force analysis shows that body separation tilting is more advantageous in power consumption than single body tilting, specifically when the payload to body weight is relatively small. Some design considerations are given for the determination of body structure and actuator powers. For the dynamic modeling, the titling balancing platform is assumed as a three-dimensional inverted pendulum with moving base and the nonlinear equation of motion is derived in terms of Kane's method. Then, a velocity/posture control loop is constructed, where the tilt angle reference is naturally generated according to the centrifugal force variation in following a circular path. Experimental results are given to validate the proposed mobile platform with the tilting control strategy.</P>
Robust Minimum-Time Control with Coarse/Fine Dual-Stage Mechanism
SangJoo Kwon,Joono Cheong 대한기계학회 2006 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.20 No.11
A robust minimum-time control (RMTC) strategy is addressed and it is extended to the dual-stage servo design. Rather than conventional switching type sub-optimal controls, it is a reference following control approach where the predetermined minimum-time trajectory (MTT) is tracked by the perturbation compensator based feedback controller. First, the minimum-time trajectory for a mass-damper system is derived. Then, the perturbation compensator to achieve robust tracking performance in spite of model uncertainty and external disturbance is suggested. The RMTC is also applied to the dual-stage positioner which consists of coarse actuator and fine one. To best utilize the actuation redundancy of the dual-stage mechanism, a null-motion controller to actively regulate the relative motion between the two stages is formulated. The performance of RMTC is validated through simulation and experiment.
Hybrid-type spatial mask–panel alignment system for manufacturing flat panel displays
Kwon, SangJoo,Hwang, Jaewoong SAGE Publications 2013 PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGIN Vol.227 No.11
<P>A hybrid-type three-dimensional visual alignment system is suggested, which is to align pattern mask and glass panel for manufacturing flat panel displays. In order to compensate for the spatial misalignments between mask and panel, a pair of decoupled positioning mechanisms are adopted, where the lower 4-PPR parallel kinematic machine provides 3-degree-of-freedom motions to the pattern mask like the current in-plane alignment systems, while the upper 4-RPS machine gives an independent 3-degree-of-freedom mobility to the glass panel along the directions excluded in the mask movement. Hence, the combinatorial motion of the two positioning mechanisms completes a spatial mask–panel alignment. In this article, two fundamental issues are solved for the operation of the hybrid alignment system. First, the inverse kinematic solutions for the hybrid parallel mechanisms are derived to determine the displacements of active joints for an arbitrary misalignment in the task space. Second, it is developed how to extract the spatial misalignment between mask and panel in real time in terms of three in-plane cameras and the focus value Gaussian model.</P>