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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Using a Kalman filter and a Pade approximation to estimate random time delays in a networked feedback control system
TL;DR: In this article, a Pade approximation is used to model time delay in the frequency domain, which can then be estimated by a Kalman filter, and applied to an inverted pendulum, where its effects are illustrated.
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Coverage Control with Information Decay in Dynamic Environments
TL;DR: In this paper, a method for coverage control for a convex region D ⊆ ℝ 2 in a dynamic environment is studied, in which the information about each point is decaying with respect to time s.t.
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An investigation on the fuel savings potential of hybrid hydraulic refuse collection vehicles
TL;DR: Fuel consumption results that indicate savings of about 20% are presented and analyzed in order to evaluate the benefit of hybrid hydraulic vehicles used for refuse collection.
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Model-Based Characterization of Inflammatory Gene Expression Patterns of Activated Macrophages.
Julia Rex,Ute Albrecht,C Ehlting,Maria Thomas,Ulrich M. Zanger,Oliver Sawodny,Dieter Häussinger,Michael Ederer,Ronny Feuer,Johannes G. Bode +9 more
TL;DR: The results provide a deepened understanding of the physiological balance leading to M1/M2 activation, indicating the relevance of co-regulatory signals at the level of Akt1 or Akt2 that may be important for directing macrophage polarization.
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Forecasts of Electric Vehicle Energy Consumption Based on Characteristic Speed Profiles and Real-Time Traffic Data
TL;DR: Within a field study with Mercedes Benz EQC experimental vehicles, it is shown that the proposed methodology can accurately predict energy consumption for long look-ahead horizons and significantly reduces the variance in prediction compared to a typical baseline strategy.