O
Oliver Sawodny
Researcher at University of Stuttgart
Publications - 644
Citations - 7417
Oliver Sawodny is an academic researcher from University of Stuttgart. The author has contributed to research in topics: Feed forward & Control theory. The author has an hindex of 34, co-authored 594 publications receiving 5895 citations. Previous affiliations of Oliver Sawodny include Audi & Technische Universität Ilmenau.
Papers
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Journal ArticleDOI
Modeling of hybrid stepper motors for closed loop operation
TL;DR: In this article, a detailed model of the hybrid stepper motor is derived, taking the saliencies of the rotor and the stator into account, and the least-squares optimal parameter values are identified for important model parameters.
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Predictive operator modeling for virtual prototyping of hydraulic excavators
TL;DR: In this article, a predictive driver model based on a state machine and decentralized model predictive controllers has been developed to solve the underlying constrained optimization problem of a 40-ton excavator.
Proceedings ArticleDOI
Characterization of a 6 DOF acoustic-inertial navigation system for minimally-invasive surgery
TL;DR: In this contribution, simulation results focussing on different noise variances, which are experimentally obtained from a prototype setup, and initial errors are presented.
Journal ArticleDOI
Virtuelle Messgeräte: Definition und Stand der Entwicklung (Virtual Measuring Instruments: Definition and Development Status)
Robert Schmitt,Friedel Koerfer,Oliver Sawodny,Jan Zimmermann,Rolf Krüger-Sehm,Min Xu,Thorsten Dziomba,Ludger Koenders,Gert Goch,Andreas Tausendfreund,Stefan Patzelt,Sven Simon,L. Rockstroh,Carsten Bellon,Andreas Staude,Peter Woias,Frank Goldschmidtböing,Martin Rabold +17 more
TL;DR: In this article, the authors present examples of current developments of virtual measuring instruments and their way of utilization is discussed, as well as a discussion of the virtual measuring process consisting of the measuring instrument, the sample, and the interaction between both.
Proceedings ArticleDOI
On the Influence of Rear Axle Steering and Modeling Depth on a Model Based Racing Line Generation for Autonomous Racing
TL;DR: It is shown, that a simplified vehicle dynamics model gives a suitable approximation of the optimal racing line compared to a simple lumped mass model and the rear axle steered car is less sensitive on modeling simplifications in comparison to the standard car.