Thermally induced deformations in electron microscopy:challenges and opportunities for system identification

Thermal effects are becoming increasingly important in efforts to enhance the\u3cbr/\u3eperformance of electron microscopes. Therefore, accurate thermal-mechanical\u3cbr/\u3emodels are desired for analysis and control. Modelling thermal systems from\u3cbr/\u3eexperimental data, i.e. system identification, is challenging due to large\u3cbr/\u3etransients, large time scales, excitation signal limitations, large environmental\u3cbr/\u3edisturbances, and nonlinear behaviour. An identification framework has been\u3cbr/\u3edeveloped to address these issues. The presented approach facilitates the\u3cbr/\u3eimplementation of advanced control techniques and error compensation\u3cbr/\u3estrategies by providing high-fidelity models

Thermo-mechanical behavior in precision motion control:unified framework for fast and accurate FRF identification

The achievement of higher accuracy and throughput in mechatronic systems using motion control has led to the situation where the thermal effects in mechatronic systems have become increasingly important and have to be actively controlled. In view of achieving overall control performance of interacting thermal and mechanical dynamics, in this paper it is aimed to develop an identification approach that delivers the required model for thermo-mechanical control. A novel technique is developed that leads to a significant reduction in both the estimation error and measurement time compared to traditional identification methods. The proposed approach is applied to a thermo-mechanical system in an extensive experimental study

Improved Local Rational Method by incorporating system knowledge:with application to mechanical and thermal dynamical systems

\u3cp\u3eA key step in experimental modeling of mechatronic systems is Frequency Response Function (FRF) identification. Applying these techniques to systems where measurement time is limited leads to a situation where the accuracy is deteriorated by transient dynamics. The aim of this paper is to develop a local parametric modeling technique that improves the identification accuracy of a range of systems by exploiting prior knowledge. The method is to impose a prior on the approximate locations of the system poles. This leads to better fit results and enables an accurate variance characterization. As a special case, traditional LPM is recovered.\u3c/p\u3

Temperature-Dependent Modeling of Thermoelectric Elements

Active thermal control is crucial in achieving the required accuracy and throughput in many industrial applications, e.g., in the medical industry, high-power lighting industry, and semiconductor industry. Thermoelectric Modules (TEMs) can be used to both heat and cool, alleviating some of the challenges associated with traditional electric heater based control. However, the dynamic behavior of these modules is non-affine in their inputs and state, complicating their implementation. To facilitate advanced control approaches a high fidelity model is required. In this work an approach is presented that increases the modeling accuracy by incorporating temperature dependent parameters. Using an experimental identification procedure, the parameters are estimated under different operating conditions. The resulting model achieves superior accuracy for a wide range of temperatures, demonstrated using experimental validation measurements