15 November 2017 Design of an adaptive regulator for an automated microscope stage
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Proceedings Volume 10605, LIDAR Imaging Detection and Target Recognition 2017; 106054F (2017) https://doi.org/10.1117/12.2296365
Event: LIDAR Imaging Detection and Target Recognition 2017, 2017, Changchun, China
Abstract
Although traditional optical microscopes have simple structures, it plays an important role in the fields of scientific research and laboratories. Microscope Stage is an important part of the microscope and the traditional stage is adjusted manually by operator, which makes the stage not arrive at the desired position accurately and smoothly due to human errors. In order to alleviate this problem, an adaptive regulator for an automated microscope stage is proposed to control the stage movement online to reach the operator desired position in this paper. First, the base controller is designed to stabilize the closed loop system, then the base controller is parameterized with Q parameters in a set of all stabilizing controller and the design of the regulator is formulated within a Youla-Kucera parameterized set of stabilizing controllers and an adaptive algorithm is designed to search online the optimal Q parameters in the controller in order to make the stage move to the desired position of operator that can achieve regulation against the unknown exogenous signals and provide the desired movement trajectory. Finally, the simulation model is built and the performance of the designed controller is verified through simulation. The results illustrate that the capability of the designed regulator for an automated microscope stage to make the stage move to the desired position smoothly and precisely and eliminate the external disturb signals. It is proved effectively that this method can be used for position adjustment of automatic microscope stage.
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Feng Li, Xiao Zhang, Rongfu Zhang, Xiaofei Qin, "Design of an adaptive regulator for an automated microscope stage", Proc. SPIE 10605, LIDAR Imaging Detection and Target Recognition 2017, 106054F (15 November 2017); doi: 10.1117/12.2296365; https://doi.org/10.1117/12.2296365
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