This image shows Julian Berberich

Julian Berberich

M.Sc.

Lecturer (akademischer Rat)
Institute for Systems Theory and Automatic Control

Contact

+49 711 685 67747
+4971168567735

Pfaffenwaldring 9
70569 Stuttgart
Germany
Room: 2.235

Office Hours

On appointment in my personal webex room

Subject

My research interests are centered around systems and control theory with a focus on 
  • data-driven and learning-based control: deriving theoretical guarantees for the closed loop, also in the presence of noisy data or nonlinearities;
  • quantum computing through the lens of control theory: studying robustness of quantum algorithms.

Google Scholar profile

  1. (Journal-) Articles

    1. Berberich, J. ; Köhler, J. ; Müller, M. A. ; Allgöwer, F.: Linear tracking MPC for nonlinear systems part II: the data-driven case. In: IEEE Trans. Automat. Control, IEEE Trans. Automat. Control. Bd. 67 (2022), Nr. 9, S. 4406–4421. — extended version on arXiv:2105.08567
    2. Pauli, P. ; Berberich, J. ; Allgöwer, F.: Robustness analysis and training of recurrent neural networks using dissipativity theory. In: at - Automatisierungstechnik, at - Automatisierungstechnik. Bd. 70 (2022), Nr. 8, S. 730–739
    3. Berberich, J. ; Köhler, J. ; Müller, M. A. ; Allgöwer, F.: Linear tracking MPC for nonlinear systems part I: the model-based case. In: IEEE Trans. Automat. Control, IEEE Trans. Automat. Control. Bd. 67 (2022), Nr. 9, S. 4390–4405
    4. Pauli, P. ; Koch, A. ; Berberich, J. ; Kohler, P. ; Allgöwer, F.: Training Robust Neural Networks using Lipschitz Bounds. In: IEEE Control Systems Lett., IEEE Control Systems Lett. Bd. 6 (2021), S. 121–126
    5. Koch, A. ; Berberich, J. ; Allgöwer, F.: Provably robust verification of dissipativity properties from data. In: IEEE Transactions on Automatic Control, IEEE Transactions on Automatic Control. (2021). — early access
    6. Bongard, J. ; Berberich, J. ; Köhler, J. ; Allgöwer, F.: Robust stability analysis of a simple data-driven model predictive control approach. In: IEEE Trans. Automat. Control, IEEE Trans. Automat. Control. (2021). — accepted, preprint: arXiv:2103.00851
    7. Berberich, J. ; Köhler, J. ; Müller, M. A. ; Allgöwer, F.: Data-driven model predictive control with stability and robustness guarantees. In: IEEE Trans. Automat. Control, IEEE Trans. Automat. Control. Bd. 66 (2021), Nr. 4, S. 1702–1717
    8. Berberich, J. ; Köhler, J. ; Müller, M. A. ; Allgöwer, F.: Data-driven model predictive control: closed-loop guarantees and experimental results. In: at-Automatisierungstechnik, at-Automatisierungstechnik. Bd. 69 (2021), Nr. 7, S. 608–618
    9. Wang, Xin ; Berberich, Julian ; Sun, Jian ; Wang, Gang ; Allgöwer, Frank ; Chen, Jie: Data-Driven Control of Event-and Self-Triggered Discrete-Time Systems. In: Automatica, Automatica. (2021). — submitted, preprint: arXiv:2202.08019
    10. Koch, Anne ; Berberich, Julian ; Köhler, Johannes ; Allgöwer, Frank: Determining optimal input–output properties: A data-driven approach. In: Automatica, Automatica. Bd. 134 (2021), S. 109906
    11. Wang, Xin ; Sun, Jian ; Berberich, Julian ; Wang, Gang ; Allgöwer, Frank ; Chen, Jie: Data-driven Control of Dynamic Event-triggered Systems with Delays. In: IEEE Trans. Automat. Control, IEEE Trans. Automat. Control. (2021). — submitted, preprint: arXiv:2110.12768
    12. Berberich, J. ; Köhler, J. ; Allgöwer, F. ; Müller, M. A.: Dissipativity properties in constrained optimal control: A computational approach. In: Automatica, Automatica. Bd. 114 (2020), S. 108840
    13. Köhler, J. ; Schwenkel, L. ; Koch, A. ; Berberich, J. ; Pauli, P. ; Allgöwer, F.: Robust and optimal predictive control of the COVID-19 outbreak. In: Annual Reviews in Control, Annual Reviews in Control. Bd. 51 (2020), S. 525–539
    14. Berberich, J. ; Scherer, C. W. ; Allgöwer, F.: Combining prior knowledge and data for robust controller design. In: IEEE Trans. Automat. Control, IEEE Trans. Automat. Control. (2020). — submitted, preprint: arXiv:2009.05253
    15. Romer, A. ; Berberich, J. ; Köhler, J. ; Allgöwer, F.: One-shot verification of dissipativity properties from input-output data. In: IEEE Control Systems Lett., IEEE Control Systems Lett. Bd. 3 (2019), S. 709–714
    16. Berberich, J. ; Dietrich, J. W. ; Hoermann, R. ; Müller, M. A.: Mathematical modeling of the pituitary-thyroid feedback loop: role of a TSH-T3-shunt and sensitivity analysis. In: Frontiers in Endocrinology, Frontiers in Endocrinology. Bd. 9 (2018), S. 91
    17. Berberich, J. ; Köhler, J. ; Allgöwer, F. ; Müller, M. A.: Indefinite Linear Quadratic Optimal Control: Strict Dissipativity and Turnpike Properties. In: IEEE Control Systems Lett., IEEE Control Systems Lett. Bd. 2 (2018), Nr. 3, S. 399–404
  2. Conference papers

    1. Köhler, M. ; Berberich, J. ; Müller, M. A. ; Allgöwer, F.: Data-driven distributed MPC of dynamically coupled linear systems. In: Proc. 25th IFAC Int. Symp. Mathematical Theory of Networks and Systems (MTNS), Proc. 25th IFAC Int. Symp. Mathematical Theory of Networks and Systems (MTNS). Bayreuth, Germany, 2022, S. 365–370
    2. Wildhagen, S. ; Berberich, J. ; Hirche, M. ; Allgöwer, F.: Improved stability conditions for systems under aperiodic sampling: model- and data-based analysis. In: Proc. 60th IEEE Conf. on Decision and Control (CDC), Proc. 60th IEEE Conf. on Decision and Control (CDC). Austin, TX, USA, 2021, S. 5788–5794
    3. Strässer, R. ; Berberich, J. ; Allgöwer, F.: Data-Driven Control of Nonlinear Systems: Beyond Polynomial Dynamics. In: Proc. 60th IEEE Conf. Decision and Control (CDC), Proc. 60th IEEE Conf. Decision and Control (CDC). Austin, TX, USA, 2021, S. 4344–4351
    4. Berberich, J. ; Köhler, J. ; Müller, M. A. ; Allgöwer, F.: On the design of terminal ingredients for data-driven MPC. In: Proc. 7th IFAC Conf. Nonlinear Model Predictive Control (NMPC), Proc. 7th IFAC Conf. Nonlinear Model Predictive Control (NMPC). Bratislava, Slovakia, 2021, S. 257–263
    5. Berberich, J. ; Wildhagen, S. ; Hertneck, M. ; Allgöwer, F.: Data-driven analysis and control of continuous-time systems under aperiodic sampling. In: Proc. 19th IFAC Symp. System Identification (SYSID), Proc. 19th IFAC Symp. System Identification (SYSID). Padova, Italy, 2021, S. 210–215
    6. Pauli, P. ; Köhler, J. ; Berberich, J. ; Koch, A. ; Allgöwer, F.: Offset-free setpoint tracking using neural network controllers. In: Proc. 3rd Conf. on Learning for Dynamics and Control (L4DC), Proc. 3rd Conf. on Learning for Dynamics and Control (L4DC). Zurich, Switzerland, 2021, S. 992–1003
    7. Alsalti, M. ; Berberich, J. ; Lopez, V. G. ; Allgöwer, F. ; Müller, M. A.: Data-Based System Analysis and Control of Flat Nonlinear Systems. In: Proc. 60th IEEE Conf. Decision and Control (CDC), Proc. 60th IEEE Conf. Decision and Control (CDC). Austin, TX, USA, 2021, S. 1484–1489
    8. Pauli, P. ; Gramlich, D. ; Berberich, J. ; Allgöwer, F.: Linear systems with neural network nonlinearities: Improved stability analysis via acausal Zames-Falb multipliers. In: Proc. 60th IEEE Conf. on Decision and Control (CDC), Proc. 60th IEEE Conf. on Decision and Control (CDC). Austin, TX, USA, 2021, S. 3611–3618
    9. Wieler, N. ; Berberich, J. ; Koch, A. ; Allgöwer, F.: Data-driven controller design via finite-horizon dissipativity. In: Proc. 3rd Learning for Dynamics and Control Conf. (L4DC), Proc. 3rd Learning for Dynamics and Control Conf. (L4DC). Bd. 144. Zürich, Switzerland : PMLR, 2021, S. 287–298
    10. Berberich, J. ; Köhler, J. ; Müller, M. A. ; Allgöwer, F.: Robust constraint satisfaction in data-driven MPC. In: Proc. 59th IEEE Conf. Decision and Control (CDC), Proc. 59th IEEE Conf. Decision and Control (CDC). Jeju, South Korea, 2020, S. 1260–1267
    11. Berberich, J. ; Köhler, J. ; Müller, M. A. ; Allgöwer, F.: Data-driven tracking MPC for changing setpoints. In: Proc. 21st IFAC World Congress, Proc. 21st IFAC World Congress. Berlin, Germany, 2020, S. 971–976
    12. Koch, A. ; Berberich, J. ; Allgöwer, F.: Verifying dissipativity properties from noise-corrupted input-state data. In: Proc. 59th IEEE Conf. on Decision and Control (CDC), Proc. 59th IEEE Conf. on Decision and Control (CDC). Jeju, South Korea, 2020, S. 616–621
    13. Berberich, J. ; Koch, A. ; Scherer, C. W. ; Allgöwer, F.: Robust data-driven state-feedback design. In: Proc. American Control Conf. (ACC), Proc. American Control Conf. (ACC). Denver, CO, USA, 2020, S. 1532–1538
    14. Venkatasubramanian, J ; Köhler, J. ; Berberich, J. ; Allgöwer, F.: Robust dual control based on gain scheduling. In: Proc. 59th IEEE Conf. Decision and Control (CDC), Proc. 59th IEEE Conf. Decision and Control (CDC). Jeju, South Korea, 2020, S. 2270–2277
    15. Berberich, J. ; Allgöwer, F.: A trajectory-based framework for data-driven system analysis and control. In: Proc. European Control Conf. (ECC), Proc. European Control Conf. (ECC). Saint Petersburg, Russia, 2020, S. 1365–1370
    16. Berberich, J. ; Sznaier, M. ; Allgöwer, F.: Signal estimation and system identification with nonlinear dynamic sensors. In: 3rd IEEE Conf. Control Technology and Applications (CCTA), 3rd IEEE Conf. Control Technology and Applications (CCTA). Hong Kong, China, 2019, S. 505–510

 


Since 06/2022 Lecturer (akademischer Rat) at the Institute for Systems Theory and Automatic Control, University of Stuttgart, Germany.
03/2018-05/2022 Research and teaching assistant at the Institute for Systems Theory and Automatic Control, University of Stuttgart, Germany.
05/2017-02/2018 Student assistant at the Institute for Systems Theory and Automatic Control, University of Stuttgart, Germany.
01/2017−06/2017 Exchange studies at the KTH Stockholm, Sweden.
06/2016-01/2017 Student assistant at the Institute for Systems Theory and Automatic Control, University of Stuttgart, Germany.
10/2015-02/2018

Studies in Engineering Cybernetics (M.Sc.) at the University of Stuttgart, Germany.

  • Master Thesis:
    "Indefinite linear quadratic optimal control: Periodic dissipativity and turnpike properties"
10/2012-09/2015

Cooperative studies in Electrical Engineering (B.Eng.) at the DHBW Mannheim, Germany, in cooperation with WITTENSTEIN SE, Igersheim, Germany.

  • Bachelor Thesis:
    "Entwicklung und Implementierung eines geberlosen Anlaufverfahrens für permanentmagneterregte Synchronmaschinen durch Messung von raumzeigermodulierten Spannungssignalen im Spannungsbereich bis 60 V"

         

To the top of the page