Abstract
To determine the planar motion of a 6-DOF precision stage, a measurement system based on three Hall sensors is adopted to obtain the X, Y, Rz motions of the stage. The machining and assembly errors in the actual mechanical system, which are difficult to measure directly, cause the parameters in the model of the Hall measurement system to deviate from their designed values. Additionally, the vertical movement of the stage will render the measurement model nonlinear. To guarantee the accuracy of the measurement, the parameters in the measurement model should be estimated and the nonlinearity compensated. In this paper, a novel approach based on self-adaptive hybrid TLBO (teaching-learning-based-optimization) is proposed to estimate the parameters in the Hall measurement model. The influences of zero deviations and vertical movements on the measurement accuracy are analyzed and compensated. The effectiveness of the proposed method is validated by experimental results obtained on a 6-DOF precision stage. Thanks to parameter estimation and calibration, the measurement error of the Hall sensor array is reduced to 6 micrometers.
Highlights
Precision motion stage is the kernel part of an ultra-precision system which has been playing a critical role in Integrated Circuit (IC) manufacturing, optical components production, Liquid Crystal
Since the 6-DOF displacements of a stage cannot be measured by a single sensor, a measurement system composed of multiple sensors is necessary
To calibrate the Hall sensor array, a 3-DOF laser interferometer array is adopted to provide reference. Environmental variations, such as temperature, humidity and vibration, limited the static accuracy of the interferometer to 500 nm, it is still adequate for calibration of the Hall sensor array
Summary
Precision motion stage is the kernel part of an ultra-precision system which has been playing a critical role in Integrated Circuit (IC) manufacturing, optical components production, Liquid Crystal. These errors will degrade the positioning accuracy of the nano-or-micro-precision stage Since such mechanical parameters are hard to directly measure due to limitation of space and measurement principle, a “soft sensor” method is proposed. Some parameter estimation methods based on the prior model structures, such as Luenberger observer, Kalman filter, and adaptive observer, have been well developed and applied in practice. The fitness function is determined, they could be in practice algorithms have been proposed and effectively applied in many fields, such as mechanical design without additional constraints. Owing to their simplicity, various kinds of meta-heuristic algorithms and trajectory planning of robotics.
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