Identification and Tracking‐Control for an Optomechatronical Image Derotator Using Neural Networks. Issue 1 (25th October 2016)

• Record Type: Journal Article
• Authors: Altmann, Bettina; Rohloff, Benjamin; Pape, Christian; Reithmeier, Eduard
• Is Part Of: Proceedings in applied mathematics and mechanics. Volume 16:Issue 1(2016)
• Page Start: 795
• Page End: 796
• Publication Date: 2016-10-25
• Subjects: Applied mathematics -- Periodicals; Engineering mathematics -- Periodicals; Mathematical physics -- Periodicals; 519
• DOI: 10.1002/pamm.201610386
• Languages: English
• ISSNs: 1617-7061

Abstract

Abstract: In this paper an optomechatronical image derotator is used for vibration measurements on rotating objects. First of all, the concept of the derotator is explained and it is shown that the phase position and the rotational velocity of the derotator and the measurement object have to be aligned. Therefore, a highly dynamic tracking‐control is needed. Considering the nonlinear friction of the synchronous motor, a model of the system which considers this non‐linearity is evolved. This is accomplished by using neural networks for the approximation of the friction term. In this case General Regression Neural Networks (GRNN) are used for the learning algorithm. Moreover, the system's parameters, eg. the friction term and the inertia, are identified based on the nonlinear model. Then a feedback control is designed by using the controllable canonical form through feedback linearization. Finally, the results of vibration measurements on a rotating blisk using the nonlinear control concept for the derotator are shown. (© 2016 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)