On Frequency Response Function Identification for Advanced Motion Control

A key step in control of precision mechatronic systems is Frequency Response Function (FRF) identification. The aim of this paper is to illustrate relevant developments and solutions for FRF identification for advanced motion control. Specifically dealing with transient and/or closed-loop conditions that can normally lead to inaccurate estimation results. This yields essential insights for FRF identification for advanced motion control that are illustrated through a simulation study and validated on an experimental setup.Comment: 6 pages, IEEE 16th International Workshop on Advanced Motion Control, 202

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.Comment: 6 pages, 21st IFAC World Congress 202

An Expert Position Paper from the Special Interest Group on Sensitive Skin of the International Forum for the Study of Itch

Sensitive skin is a frequent complaint in the general population, in patients, and among subjects suffering from itch. The International Forum for the Study of Itch (IFSI) decided to initiate a special interest group (SIG) on sensitive skin. Using the Delphi method, sensitive skin was defined as “A syndrome defined by the occurrence of unpleasant sensations (stinging, burning, pain, pruritus, and tingling sensations) in response to stimuli that normally should not provoke such sensations. These unpleasant sensations cannot be explained by lesions attributable to any skin disease. The skin can appear normal or be accompanied by erythema. Sensitive skin can affect all body locations, especially the face”. This paper summarizes the background, unresolved aspects of sensitive skin and the process of developing this definition

Incorporating Prior Knowledge in Local Parametric Modeling for Frequency Response Measurements: Applied to Thermal/Mechanical Systems

Frequency response function (FRF) identification is a key step in experimental modeling of many applications, including mechatronic systems. Applying these techniques to systems where measurement time is limited leads to a situation where the accuracy of the identified model is deteriorated by transient dynamics. This article aims to develop an identification procedure that mitigates these transient dynamics by employing local parametric modeling techniques. To improve the modeling accuracy, prior knowledge is suitably incorporated in the procedure while at the same time allowing for rational parameterizations that maintain a closed-form solution. The prior knowledge is exploited in a relevant local frequency range using a specific Möbius transformation. Preexisting methods, including the commonly used local polynomial method, are recovered as a special case. The presented framework leads to accurate identification results in a simulation study as well as on experimental measurement data

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

A 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