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Automatic Control and System Dynamics
Automatic Control and System Dynamics

Dr.-Ing. Pavel Osinenko


Room: W137, Weinhold-Bau
Telephone: +49 371 531-30325
Telefax: +49 371 531-830325
Mail: pavel.osinenko@...
Office hours: by arrangement


Ongoing projects

  • BigApple: control and systems theory for optimization of apple production
  • See description here

  • Sensor vehicle
  • See description here

  • Predictive control and reinforcement learning
  • The major goal is to study optimal control of systems with unknown dynamics from the perspective of reinforcement learning. Some important questions are: to what extent can artificial controllers imitate the process of natural adaptation in an unknown environment? What can imitate natural reward and punishment? What may be an analog of natural energy optimization?

    Reinforcement learning is an approach to optimal control that tries to predict the behavior of the cost function whereby the prediction has an infinite extent in time. The problem is open-horizon in the sense that the agent tries to apply such actions that all the future performance marks be optimized. Learning the behavior of the system and the optimal cost are the core of the approach. Specific learning methods range from simple polynomial models to artificial neural nets.

    Within the project, students study and perform simulations and laboratory investigations with the most actual reinforcement learning methods. Thereby they discover similarities between different advanced optimal control approaches. The project offers great educational potential in identification and optimal control theory.

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  • Constructive control theory
  • Addressing mathematical foundations of control theory from the standpoint of constructive mathematics, whereby formal verification of control systems and implementation of control-theoretic theorems in proof assistants such as Coq and Minlog is of special importance. Students with good skills in mathematics and programming are welcome to participate. Example publications: 1, 2, 3.

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  • P. Osinenko, S. Streif (2017). Constructive approximate extremum value theorem for function spaces. arXiv. Available here
  • P. Osinenko, S. Streif (2017). Optimal traction control for heavy-duty vehicles. Control Engineering Practice, 69. Pp. 99-111.
  • P. Osinenko, T. Göhrt, G. Devadze, S. Streif (2017). Stacked model-free adaptive dynamic programming using Kalman-filter estimation. In Proceedings of the 20th IFAC World Congress. Toulouse, France
  • P. Osinenko, T. Göhrt, G. Devadze, S. Streif (2017). Constructive analysis of control systems stability. In Proceedings of the 20th IFAC World Congress. Toulouse, France
  • P. Osinenko, S. Streif (2016). A note on Brehm's extension theorem. arXiv. Available here
  • P. Osinenko, G. Devadze, S. Streif (2016). A note on constructive treatment of eigenvectors. arXiv. Available here
  • P. Osinenko, M. Geißler, T. Herlitzius, S. Streif (2016). Experimental results of slip control with a fuzzy–logic–assisted unscented Kalman filter for state estimation. In 2016 IEEE FUZZ International Conference on. Pp. 501-507. Vancouver, Canada
  • T. Bögel, P. Osinenko, T. Herlitzius (2016). Assessment of soil roughness after tillage using spectral analysis. Soil and Tillage Research, 159. Pp. 73-82. doi:10.1016/j.still.2016.02.004
  • P. Osinenko, M. Geißler, T. Herlitzius (2015). Fuzzy-logic assisted power management for electrified mobile machinery. Neurocomputing, 170. Pp. 439-447. doi:10.1016/j.neucom.2015.04.095
  • P. Osinenko, M. Geißler, T. Herlitzius (2015). A method of optimal traction control for farm tractors with feedback of drive torque. Biosystems Engineering, 129. Pp. 20-33. doi:10.1016/j.biosystemseng.2014.09.009
  • P. Osinenko, M. Geißler, T. Herlitzius (2015). HIL-tests with Slip Control for Electric Single Wheel-Drives – Results and Traction Test Stand Concepts. In 73rd conference LAND.TECHNIK - AgEng 2015. Hannover, Germany
  • M. Geißler, P. Osinenko, T. Herlitzius (2015). Winding Switching Strategy for Electric Wheel Drives in Agricultural Machinery. In 2015 IEEE International Conference on Industrial Technology (ICIT 2015). Pp. 851-856. Seville, Span
  • P. Osinenko, M. Geißler, T. Herlitzius (2014). Adaptive unscented Kalman filter with a fuzzy supervisor for electrified drive train tractors. In 2014 IEEE International Conference on Fuzzy Systems (FUZZ IEEE). Pp. 322-331. Beijing, China
  • P. Osinenko, M. Geißler, T. Herlitzius (2013). Real-time identification of vehicle dynamics for mobile machines with electrified drive trains. In 71st conference LAND.TECHNIK - AgEng 2015. Pp. 241-248. Hannover, Germany
  • M. Geißler, P. Osinenko, J. Scholz (2013). Potentials of vehicle dynamics and slip control for mobile machinery enabled by an electric 4WD [Potenzial von Fahrdynamik- und Schlupfregelung am elektrischen Einzelradantrieb von Arbeitsmaschinen – am Beispiel Rigitrac]. In Kolloquium Elektrische Antriebe in der Landtechnik. Wieselburg, Austria
  • S. Tetzlaff, M. Geißler, P. Osinenko (2013). Simulation for evaluation of potentials of electrified drives in implements [Simulation zur Potenzialabschätzung eines elektrifizierten Geräteantriebes]. In 4th Confernce Hybridantriebe für mobile Arbeitsmaschinen. Pp. 73-83. Karlsruhe, Germany
  • H. Döll, M. Geißler, T. Herlitzius (2012). Multiple axles: a means of reducing soil compaction and improving traction [Mehr Achsen: ein Mittel zur Verringerung der Bodenbelastung und Verbesserung der Zugkrafteffizienz]. In 2nd Commercial Vehicle Technology Symposium. Pp. 307-316. Kaiserslautern, Germany


  • P. Osinenko, M. Geißler und T. Herlitzius (2015). Apparatus and method for control of vehicle propulsive force [Vorrichtung und Verfahren zur Regelung von Vortriebskräften bei Fahrzeugen]. German patent registration A3765/A3886