Systems Theory and Automatic Control

Bruno Morabito


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



Phone:

(+49)0391/6758019

Email:

Research Interests

Model Predictive Control, machine learning and state estimation with applications on bioreactors and trajectory drilling

Publications / Presentations

Submitted Journals Articles

[1] J. Pohlodek, B. Morabito, C. Schlauch, P. Zometa, and R. Findeisen. Flexible development and evaluation of machine-learning-supported optimal control and estimation methods via HILO-MPC. 2022. Preprint available. [ DOI ]

Proceedings (peer reviewed)

[13] 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. IFAC-PapersOnLine, 54(6):321--328, 2021. 7th IFAC Conference on Nonlinear Model Predictive Control NMPC 2021. [ DOI | http ]
[12] J. Bethge, B. Morabito, H. Rewald, A. Ahsan, S. Sorgatz, and R. Findeisen. Model predictive control of mixed traffic at intersections using learning and classification of human driving behavior. In Proceedings of IFAC World Congress, pages 14557--14563, 2020.
[11] B. Morabito, M. Kögel, S. Blasi, V. Klemme, C. Hansen, O. Höhn, and R. Findeisen. Multi-stage event-triggered model predictive control for automated trajectory drilling. volume 53, pages 9478--9483, 2020. 21th IFAC World Congress. [ DOI | http ]
[10] H. H. Nguyen, B. Morabito, and R. Findeisen. Repetitive set-based learning robust predictive control for lur'e systems. In Proceedings of IFAC World Congress, pages 7117--7122, Berlin, Germany, 2020.
[9] J. Pohlodek, A. Rose, B. Morabito, L. Carius, and R. Findeisen. Data-driven Metabolic Network Reduction for Multiple Modes Considering Uncertain Measurements. IFAC-PapersOnLine, 53(2):16866--16871, 2020. 21st IFAC World Congress. [ DOI ]
[8] B. Morabito, A. Kienle, R. Findeisen, and L. Carius. Multi-mode model predictive control and estimation for uncertain biotechnological processes. In 12th IFAC Symposium on Dynamics and Control of Process Systems, including Biosystems DYCOPS 2019, volume 52, pages 709 -- 714, July 2019. [ DOI | http ]
[7] 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.
[6] 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.
[5] L. Carius, J. Pohlodek, B. Morabito, A. Franz, M. Mangold, R. Findeisen, and A. Kienle. Model-based state estimation utilizing a hybrid cybernetic model. In Advanced Control of Chemical Processes (AdChem), July 2018.
[4] P. Andonov, B. Morabito, A. Savchenko, and R. Findeisen. Admissible Control Parametrization of Uncertain Finite-time Processes With Application to Li-ion Battery Management. In European Control Conference (ECC), pages 2338--2343, Limasol, Cyprus, 2018.
[3] 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 ]
[2] B. Morabito, R. Klein, and R. Findeisen. Real time feasibility and performance of moving horizon estimation for li-ion batteries based on first principles electrochemical models. In 2017 American Control Conference (ACC), pages 3457--3462, May 2017. [ DOI ]
[1] B. Morabito, M. Kögel, E. Bullinger, G. Pannocchia, and R. Findeisen. Simple and efficient moving horizon estimation based on the fast gradient method. IFAC-PapersOnLine, 48(23):428 -- 433, 2015. 5th IFAC Conference on Nonlinear Model Predictive Control NMPC 2015. [ DOI | http ]

Submitted Proceedings (accepted)

[5] S. Espinel-Rios, E. Huber, A. Alcalá-Orozco, B. Morabito, T. Rexer, U. Reichl, S. Klamt, and R. Findeisen. Cell-free biosynthesis meets dynamic optimization and control: a fed-batch framework. IFAC-PapersOnLine (to appear), 2022.
[4] S. Espinel-Rios, B. Morabito, K. Bettenbrock, and R. Findeisen. Soft sensor for monitoring dynamic changes in cell composition. IFAC-PapersOnLine (to appear), 2022.
[3] S. Espinel-Rios, B. Morabito, J. Pohlodek, K. Bettenbrock, S. Klamt, and R. Findeisen. Optimal control and dynamic modulation of the ATPase gene expression for enforced ATP wasting in batch fermentations. IFAC-PapersOnLine (to appear), 2022.
[2] B. Morabito, H. H. Nguyen, J. Matschek, and R. Findeisen. Safe exploration using learning supported tube-based robust model predictive control for repetitive processes. Submitted to ACC 2022.
[1] B. Morabito, J. Pohlodek, L. Kranert, S. Espinel-Rios, and R. Findeisen. Efficient and simple gaussian process supported stochastic model predictive control for bioreactors using hilo-mpc. Submitted to DYCOPS 2022.

Patent applications

[1] F. Quattrone, C. Hansen, O. Hoehn, J. Koeneke, B. Morabito, and R. Findeisen. Model-based parameter estimation for directional drilling in wellbore operations, 2019. Application number: US15935659. [ .pdf ]

Supervised Master's Thesis

  • Charge Optimisation of Electric Vehicles using Machine Learning Supported Model Predictive Control - Supriya Rao Gude (2020)
  • Multi-mode Model-Predictive Control of an Autonomous Vehicle at an Intersection - Syed Adil Muddassir Ahsan (2019)
  • Directional prediction using drill bit steerability models within a trajectory control system - Muhammad Adnan Hanif (2019)
  • Predictive Control for Virtual Reality Stereo Camera Tracking - Denis Babaev (2017)
  • Model Predictive Control for Trajectory Tracking of an Unmanned Quadcopter - Ruzailya Gilyazetdinova (2017)
  • Nichtlineare modellpraediktive Regelung fuer Pfandverfolgungsprobleme im Bereich Tiefbohrtechnik unter Berueksichtigung von Beschraenkungen und Kommunikationskosten- Philip Trennt (2016)
   Go to Top

Supervised Projects

  • Literature review on machine learning method for bioreactors modelling and control - Siddharth Patel (2020)

CV

Research Experiences

  • 2016 - Currently: Member of the International Max Planck Research School (IMPRS), Max Planck Institute - Magdeburg, Germany

  • 2016 - Currently: Research assistant, Otto von Gueriche University, Magdeburg, Germany

  • August 2015 - March 2016: Internship at Robert Bosch Research and Technology Center, Palo Alto, California, USA

  • September 2013 - March 2014: Student assistent, Otto von Guericke University, Magdeburg, Germany

Education

  • 2012 - 2014: Master's in Chemical Engineering, University of Pisa, Pisa, Italy

  • 2009 - 2012: Bachelor's in Chemical Engineering, University of Pisa, Pisa, Italy

  • 2004 - 2009: High school "A.Panella" Chemistry Section, Reggio Calabria, Italy

Teaching

  • Optimal Control tutorials WS 2019-2019
  • Praktikum Regulungstechnik SS 2018
  • Optimal Control tutorials WS 2017-2018
  • Praktikum Regulungstechnik SS 2017
  • Optimal Control tutorials WS 2016-2017
   Go to Top

Contact: email to sekretariat.findeisen@ovgu.de - Impressum