Lehrstuhl SRS - Team

Dr.-Ing. Mark Spiller

Mark Spiller

DLR

Kurzvita / CV

2019Dr.-Ing. Mechanical Engineering,
Faculty of Engineering,
University of Duisburg-Essen, Germany
2013Dipl.-Ing. Mechanical Engineering/Mechatronics,
Faculty of Engineering,
University of Duisburg-Essen, Germany

Veröffentlichungen / Publications

  • Bakhshande, F.; Spiller, M.; Hering, J.; Söffker, D.: Computationally efficient neural network-based model predictive control for real-time implementation . ASME IDETC , Boston, USA, August 20-23rd, 2023, accepted.
  • Spiller, M.; Söffker, D.: Robust Control of Relative Degree Two Systems Subject to Output Constraints with Time-Varying Bounds. 60th IEEE Conference on Decision and Control (CDC), Austin, Texas, 2021, pp. 5402-5409.
    • , [PDF], [Link]
  • Spiller, M.; Söffker, D.: Stator-Rotor Contact Force Estimation of Rotating Machine Automation. Automation, Vol. 2, No. 3, 2021, pp. 83-97.
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  • Thind, N.S.; Spiller,M.; Söffker, D.: Data-driven prediction of inland vessel trajectories. Autonomous Inland and Short Sea Shipping Conference (AISS), Duisburg, Okt 15, 2021.
  • Spiller, M.; Söffker, D.: Chattering mitigated sliding mode control of uncertain nonlinear systems. Proc. of the IFAC World Congress 2020, Berlin, Germany, 2020.
    • , [PDF], [Link]
  • Spiller, M.; Söffker, D.: Output constrained sliding mode control: A variable gain approach. Proc. of the IFAC World Congress 2020, Berlin, Germany, 2020.
    • , [PDF], [Link]
  • Spiller, M.; Söffker, D.: Automated Target Interception Based on Multiple Object Tracking. 2020 European Control Conference , Saint Petersburg, Russia, 2020, pp. 1763-1768.
    • , [Link]
  • Bakhshande, F.; Spiller, M.; King, Y.L; Söffker, D.: Computationally Efficient Model Predictive Control for Real Time Implementation experimentally applied on a Hydraulic Differential Cylinder. Proc. of the IFAC World Congress 2020, Berlin, Germany, 2020.
    • , [PDF], [Link]
  • Spiller,M.; Bakhshande,F.; Söffker, D.: Adaptive neural network based predictive control of nonlinear systems with slow dynamics. Proceedings of the ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Virtual, August 17–19, 2020, Vol. 2, pp. V002T02A029a.
    • , [PDF], [Link]
  • Spiller, M.; Söffker, D.: On the Relation Between Smooth Variable Structure and Adaptive Kalman Filter. Frontiers in Applied Mathematics and Statistics, Vol. 6, 2020.
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  • Bakhshande, F.; Boschmann, W.; Dahlke, L.; Henn, R.; Höpken, J.; Kracht, F.; Maas, N.; Oberhagemann, J.; Schramm, D.; Sieberg, P.; Singh-Thind, N.; Söffker, D.; Spiller, M.: The AutoBin project – Key concepts, status, and intended outcomes. Autonomous Inland and Short Sea Shipping Conference - AISS2020, Duisburg, Germany, Oct 23, 2020.
  • Spiller, M.; Bakhshande, F.; Söffker, D.: The uncertainty learning filter: a revised smooth variable structure filter. Signal Processing - An International Journal, 2018, pp. 217-226.
    • , [Link]