The scanning electron microscope is becoming a popular tool to perform tasks that require positioning, manipulation, characterization, and assembly of micro-components. However, some of these applications require a higher level of performance with respect to dynamics and precision of positioning. One limiting factor is the presence of unidentified noises and disturbances. This work aims to study the influence of mechanical disturbances generated by the environment and by the microscope, identifying how these can affect elements in the vacuum chamber. To achieve this objective, a dedicated setup, including a high-resolution vibrometer, was built inside the microscope. This work led to the identification and quantification of main disturbances and noise sources acting on a scanning electron microscope. Furthermore, the effects of external acoustic excitations were analysed. Potential applications of these results include noise compensation and real-time control for high accuracy tasks.
The aim of the paper is the design and the analysis of a gain scheduled controller for an accurate and fast positioning with nanometer resolution of a nonlinear electrostatic microgripper. The controller is designed to achieve a positioning of the gripping arm from few hundred nanometers to several tens of micrometers with some performance criteria. This very large operating range is crucial for a range of microrobotics applications and has never been addressed in existing control techniques of microgrippers. The controller is designed considering noises that are relevant at the nanometer scale and nonlinearities that become significant at the micrometer scale. Therefore, a nonlinear model of the system is proposed and is reformulated into a polynomial LPV (Linear Parameter Varying) model. The most relevant source of noise to be considered for the controller synthesis is defined taking into account results from previous works. Considering the particular polynomial parametric dependence of the LPV model, a multivariable controller is designed using an affine LPV descriptor representation of the system and specific linear matrix inequalities. The efficiency of the controller and the relevance of the theoretical approach are demonstrated through experimental implementation results.
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