Systems Theory and Automatic Control

M. Sc. Janine Matschek

Ph.D. Student and Research Assistant

Institute for Automation Engineering (IFAT)
Laboratory for Systems Theory and Automatic Control
Otto-von-Guericke University Magdeburg
39106 Magdeburg, Germany


G07 - 210


(+49) 0391-67 58775


(+49) 0391-67 41191


Research Interests

My current research interests are focused on optimization based control. In particular, I am interested in:

  • Path Following with Model Predictive Control
  • Gaussian Processes and learning-based Model Predictive Control
  • Force Control and Robotics in Medical Applications


Journals Articles and Book Chapters (all peer reviewed)

[2] J. Matschek, T. Bäthge, T. Faulwasser, and R. Findeisen. Nonlinear Predictive Control for Trajectory Tracking and Path Following: An Introduction and Perspective. Handbook of Model Predictive Control, pages 169--198, 2018.
[1] J. Matschek, E. Bullinger, F. von Haeseler, M. Skalej, and R. Findeisen. Mathematical 3d modelling and sensitivity analysis of multipolar radiofrequency ablation in the spine. Mathematical biosciences, 284:51--60, 2017. [ DOI ]

Proceedings (peer reviewed)

[12] J. Matschek, T. Gonschorek, M. Hanses, N. Elkmann, F. Ortmeier, and R. Findeisen. Learning references with Gaussian processes in model predictive control applied to robot assisted surgery. In Proceedings of European Control Conference (ECC), pages 362--367, 2020.
[11] J. Matschek and R. Findeisen. Learning supported Model Predictive Control for Tracking of Periodic References. In Proceedings of Machine Learning Research, volume 120, pages 511--520, 2020.
[10] J. Matschek, R. Jordanowa, and R. Findeisen. Direct Robotic Force Control with Learning Supported Model Predictive Control. In Proceedings of Conference on Control Technology and Applications (CCTA), pages 8--13, 2020.
[9] H. H. Nguyen, J. Matschek, T. Zieger, A. Savchenko, N. Noroozi, and R. Findeisen. Towards nominal stability certification of deep learning-based controllers. In 2020 American Control Conference (ACC), 2020.
[8] M. Ibrahim, J. Matschek, B. Morabito, and R. Findeisen. Improved area covering in dynamic environments by nonlinear model predictive path following control. In Proceedings of 8th IFAC Symposium on Mechatronic Systems (MECHATRONICS 2019), Vienna, Austria, 2019.
[7] M. Ibrahim, J. Matschek, B. Morabito, and R. Findeisen. Hierarchical model predictive control for autonomous vehicle area coverage. In Proceedings of 21st IFAC Symposium on Automatic Control in Aerospace (ACA 2019), Cranfield, UK, 2019.
[6] J. Bethge, B. Morabito, J. Matschek, and R. Findeisen. Multi-mode learning supported model predictive control with guarantees. In Proceedings of 6th Nonlinear Model Predictive Control Conference (NMPC), pages 616--621, Madison, United States, 2018. [ DOI ]
[5] K. J. Kazim, J. Bethge, J. Matschek, and R. Findeisen. Combined predictive path following and admittance control. In Proceedings of American Control Conference (ACC), pages 3153--3158, Milwaukee, United States, 2018. [ DOI ]
[4] J. Matschek, J. Bethge, P. Zometa, and R. Findeisen. Force feedback and path following using predictive control: Concept and application to a lightweight robot. In Proceedings of 19th IFAC World Congress Toulouse, pages 10243--10248, Toulouse, France, 2017. [ DOI ]
[3] J. Matschek, A. Himmel, F. von Haeseler, E. Bullinger, M. Skalej, and R. Findeisen. Mathematical modelling and sensitivity analysis of multipolar radiofrequency ablation in the spine. In Proceedings of 9th IFAC Symposium on Biological and Medical Systems (BMS), pages 243--248, Berlin, Germany, 2015. [ DOI ]
[2] J. Matschek, F. von Haeseler, and R. Findeisen. Mathematical modeling of mono- and multipolar radio frequency ablation in the spine. In Proceedings of 1st Conference on Image Guided Interventions (IGIC), pages 69--70, Magdeburg, Germany, 2014.
[1] T. Faulwasser, J. Matschek, P. Zometa, and R. Findeisen. Predictive path-following control: Concept and implementation for an industrial robot. In Proc. IEEE Conf. on Cont. Appl., CCA'13, pages 128--133, Hyderabad, India, 2013.

Submitted Proceedings (accepted)

[1] J. Matschek, A. Himmel, K. Sundmacher, and R. Findeisen. Constrained Gaussian process learning for model predictive control. In Proceedings of 20th IFAC World Congress Berlin, 2020. to appear.

Submitted Proceedings (to be peer reviewed)

[3] J. Matschek, A. Himmel, and R. Findeisen. Constrained learning for model predictive control in reference tracking tasks. 2021.
[2] J. Matschek, J. Bethge, M. Soliman, B. Elsayed, and R. Findeisen. Constrained reference learning for continuous-time model predictive tracking control of autonomous systems. 2021.
[1] B. Morabito, J. Pohlodek, J. Matschek, A. Savchenko, L. Carius, and R. Findeisen. Towards risk-aware machine learning supported model predictive control and open-loop optimization for repetitive processes. Submitted to NMPC 2021.

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Teaching Activity



  • Lecturer for Regelungstechnik (Control Engineering ) (WS 2018/2019, WS 2019/2020 )
  • Assistant for Regelungstechnik (Control Engineering) (WS 2017/2018, WS 2016/2017, WS 2015/2016, WS 2014/2015, and WS 2013/2014)
  • Assistant for Regelungstechnik II/Systemtheorie (Control Engineering 2/Systems Theory) (SS2019, SS2018, SS 2017, SS 2016, SS 2015, SS 2014, and SS2013)
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Supervised student projects, Bachelor's and Master's Theses


  • Benmark alternativer Softwaretools zur intuitiven Programmierung von Robotern (P. Kallow, BA)


  • Model Predictive Controller Design for a Franka Emika Robot (P. Holzmann, MA)
  • Echtzeitfähige Kommunikation, Regelung und Inbetriebnahme des Franka Emika Robot (P. Holzmann, research project)
  • Model order reduction and validation of 7 DOF robot (B. Dahroug, research project)
  • Lernbasierte MPC mittels Gaußscher Prozessmodelle für Kontaktkräfte in der Robotik (R. Jordanova, MA)
  • Tracking model predictive control for a robotic manipulator (A. Kurmashev, MA)
  • Learning-supported multi-mode force control (P. Eskandar, MA)
  • Cooperative contract based MPC for mobile robots (A. Iakupova, MA)
  • Implementierung eines Gaußschen Prozessmodells mit NARX-Struktur innerhalb einer modellprädiktiven Kontaktkraftregelung (R. Jordanowa, research project)
  • Modellprädiktive Regelung eines Leichtbauroboters zur Bewegungskompensation (S. Fahlbusch, MA)
  • Analyse und Bewertung der Anwendbarkeit von Gaußschen Prozessmodellen auf ein Kontaktszenario in der Robotik (R. Jordanowa, BA)
  • Path-following Control of Small Unmanned Helicopter (P. Stupnitskii and S. Valieva, MA)
  • Gaußsche Regression periodischer Prozesse (S. Fahlbusch, research project)
  • Model predictive hybrid force/position control for path following in robotic applications (J. Bethge, MA)
  • Kinematic Calibration of Lightweight Robot with High Precision Demands (Z. Zhang, MA)
  • Entwicklung einer 3D-Simulation für die bipolare Radiofrequenzablation (F. Fritz, research project)
  • Modellierung und PID Reglerentwurf für Kontakte (C. Kallies, research project)
  • Unscented Kalman filtering of a robotic manipulator (J. Bethge, BA)
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