Dieter Teichrib M.Sc.
Email: dieter.teichribtu-dortmundde
Tel.: +49 231 / 755 - 5619
Room: Maschinenbaugebäude 2, Raum 317
Office hours: on request
Research interests
- machine learning for control (tailored topologies of artificial neural networks for learning-based control)
- optimal and robust control (linear/explicit/robust MPC, time-optimal control)
Teaching activities
Regelungstechnik (Übung)
Angewandte konvexe Optimierung (Übung)
Further information
01 | 2021 - today: Research assistant in the Control and Cyberphysical Systems Group, Department of Mechanical Engineering, TU Dortmund University
01 | 2019 - 12 | 2020: Studies on Electrical Engineering (M.Sc. 12 | 2020) at the Paderborn University
06 | 2019 - 09 | 2020: Scientific assistant in the Automatic Control Group, Department of Electrical Engineering, Paderborn University
07 | 2019 - 10 | 2019: Scholarship Deutschlandstipendium
10 | 2015 - 01 | 2019: Studies on Electrical Engineering (B.Sc. 01 | 2019) at the Paderborn University
01 | 2018 - 10 | 2018: Student assistant in the Signal and System Theory Group, Department of Electrical Engineering, Paderborn University
09 | 2011 - 07 | 2014: Vocational training as an Industrial Electronics Technician at the Siemens AG in Bielefeld
D. Teichrib and M. Schulze Darup. Piecewise regression via mixed-integer programming for MPC, 6th Annual Learning for Dynamics & Control Conference (L4DC), 2024 (accepted).
D. Teichrib and M. Schulze Darup, Efficient Computation of Lipschitz Constants for MPC with Symmetries, Proc. of the 62nd IEEE Conference on Decision and Control (CDC), pp. 6685-6691, 2023. DOI: 10.1109/CDC49753.2023.10383472. Preprint: arXiv:2311.04580.
D. Teichrib and M. Schulze Darup, Error bounds for maxout neural network approximations of model predictive control, IFAC-PapersOnLine, 56(2): pp. 10113-10119, 2023. DOI: 10.1016/j.ifacol.2023.10.883. Preprint: arXiv:2304.08779.
H. Nikbakht and M. T. Khoshmehr, B. van Someren, D. Teichrib, M. Hammer, J. Förstner, B. I. Akca. Asymmetric, non-uniform 3-dB directional coupler with 300-nm bandwidth and a small footprint, Optics Letters, 48(2): pp. 207-210, 2023. DOI: 10.1364/OL.476537
D. Teichrib and M. Schulze Darup, Tailored max-out networks for learning convex PWQ functions, 2022 European Control Conference (ECC), 2022. DOI: 10.23919/ECC55457.2022.9838225, Seiten: 2272-2278. Preprint: arXiv.2206.06826.
D. Teichrib and M. Schulze Darup, Tailored neural networks for learning optimal value functions in MPC, 60th IEEE Conference on Decision and Control (CDC), 2021. DOI: 10.1109/CDC45484.2021.9683528, Seiten: 5281-5287. Preprint: arXiv:2112.03975.
M. Schulze Darup and D. Teichrib, Efficient computation of RPI sets for tube-based robust mpc, Proc. of the 2019 European Control Conference (ECC), pp. 325-330, 2019. DOI: 10.23919/ECC.2019.8796265