Maik Pfefferkorn M.Sc.
Working area(s)
Machine-Learning-supported Model Predictive Control, Gaussian Processes (GPs) for Systems Modeling and Control, Uncertainty Propagation and Safety Guarantees in Gaussian-Process-based Model Predictive Control, Stochastic Model Predictive Control, Control of Scanning Quantum Dot Microscopy
Contact
maik.pfefferkorn@iat.tu-...
work +49 6151 16-25171
fax 06151 16-25172
Work
S3|10 520
Landgraf-Georg-Str. 4
64283
Darmstadt
| Master's Theses | |
|---|---|
| Topic | Status |
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Model Predictive Control for Ball Juggling (opens in new tab) Co-Supervisor: Kai Ploeger |
available |
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Decoding Linguistic Information from iEEG Spectrograms with Deep Learning Approaches Co-Supervisor: Keivan Ahmadi |
in progress |
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Learning Temporal and Spectral Patterns from iEEG Spectrograms using Deep Learning (opens in new tab) Co-Supervisor: Keivan Ahmadi |
in progress |
|
Safe but Not Conservative: Robust MPC for High-Performance Time-Optimal Control External Thesis (TU Munich, Prof. Schöllig) |
completed |
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Hybrid Force-Impedance Control for Robotic Manipulators Co-Supervisor: Junning Huang |
completed |
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Enhancing EEG-based Cognitive State Classification through Graph Neural Networks (opens in new tab) Co-Supervisor: Keivan Ahmadi |
completed |
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Augmenting EEG Data for Music-Emotion Recognition using Diffusion Models (opens in new tab) Co-Supervisor: Keivan Ahmadi |
completed |
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Formation Path Planning using Model Predictive Control (opens in new tab) Co-Supervisor: Sebastian Gasche, Christian Kallies |
completed |
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Exploring Music Perception and Imagination through Deep Learning (opens in new tab) Co-Supervisor: Keivan Ahmadi |
completed |
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Imitative Model Predictive Control for Safe Navigation Co-Supervisor: Philipp Holzmann |
completed |
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Data-driven Surrogate Model Generation for Automated Directional Drilling (opens in new tab) Co-Supervisor: Felix Häusser |
completed |
| Tire-Friction Learning for Vehicles by Gaussian Process State Space Models | completed |
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Control of a Three-Tank System using Multi-Fidelity Gaussian Processes (opens in new tab) Co-Supervisor: Felix Häusser |
completed |
|
Graph Diffusion for Imitation Learning in Robotics Co-Supervisor: Ali Younes, An T. Le |
completed |
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Stochastic Nonlinear Model Predictive Control for Offshore Energy Systems using Gaussian Processes Co-Supervisor: Kiet T. Hoang, Lars Imsland |
completed |
|
Set-up and Closed-Loop Control of an Exoskeleton Co-Supervisor: Sebastian Hirt |
completed |
| Learning Patient Models of Acute Lymphoblastic Leukemia for Individualized Maintenance Therapy | completed |
| Modeling of Cell Dynamics During Maintenance Therapy of Acute Lymphoblastic Leukemia | completed |
| Cost Function Learning for Model Predictive Control Using Bayesian Optimization | completed |
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Physically Consistent Model Learning of Robotic Systems with Gaussian Processes (opens in new tab) Co-Supervisor: Philipp Holzmann |
completed |
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Gaussian-Process-based Modeling of Human Drivers from Real Data (opens in new tab) Co-Supervisor: Johanna Bethge |
completed |
| The Fokker-Planck Equation for Gaussian Process-based Model Predictive Control (opens in new tab) | completed |
| Bachelor's Theses | |
| Topic | Status |
|
Deep Learning Approaches for Classifying EEG Responses to Naturalistic Music Stimuli (opens in new tab) Co-Supervisor: Keivan Ahmadi |
completed |
| Modeling of Human-Driven Vehicles from Real Data Using Gaussian Mixture Models | completed |
|
Gaussian-Process-based Modeling of Individual Human-Driven Vehicles from Real Data (opens in new tab) Co-Supervisor: Johanna Bethge |
completed |
| Project Seminars | |
| Topic | Status |
|
Contract-Based Hierarchical Control of a Mobile Ground Robot (opens in new tab) Co-Supervisor: Lukas Theiner |
completed |
|
Tuning of Model Predictive Control using Bayesian Optimization (opens in new tab) Co-Supervisor: Philipp Holzmann |
completed |
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Model Identification and Control for Robotic Manipulators using Physics-Informed Neural Networks (opens in new tab) Co-Supervisor: Philipp Holzmann |
completed |
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Iterative Model Improvement Learning Control for Robotic Manipulators (opens in new tab) Co-Supervisor: Philipp Holzmann |
completed |
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Formal Verification of a Robotic Arm using Hybrid Model Checking (Otto-von-Guericke University Magdeburg) |
completed |
| Proseminars | |
| Topic | Status |
| Formal Verification of Machine-Learning-supported Model Predictive Controllers | in progress |
| Optimal Risk Allocation for Stochastic Model Predictive Control | completed |
| Technical University of Darmstadt | |
|---|---|
| Model Predictive Control and Machine Learning | Winter term 2021/2022, 2022/2023, 2023/2024, 2024/2025 |
| Machine Learning for Mechatronic and Dynamical Systems | Summer term 2025 |
| Project Course: Control Theory | Summer term 2024 |
| Modeling and Simulation | Summer term 2022 |
| Otto-von-Guericke University Magdeburg | |
| Machine Learning, Dynamical Systems, and Control | Summer term 2020, 2021 |
| Introduction to Cybernetics | Winter term 2020/2021 |
| 2025 | |
|---|---|
| [16] |
S. Hirt, L. Theiner, M. Pfefferkorn, R. Findeisen: A Hierarchical Surrogate Model for Efficient Multi-Task Parameter Learning in Closed-Loop Control. Conference on Decision and Control, 2025. Accepted. |
| [15] |
Y. Zhao, M. Pfefferkorn, M. Templer, R. Findeisen: Efficient Learning of Vehicle Controller Parameters via Multi-Fidelity Bayesian Optimization: From Simulation to Experiment. IEEE Intelligent Vehicles Symposium, pages 1807 – 1813, 2025. |
| [14] |
K. Ahmadi, M. Pfefferkorn, S. Sorge, R. Findeisen: Leveraging Graph Neural Networks to Decode Music-Induced Emotions from EEG. International Conference of the IEEE Engineering in Medicine and Biology Society, 2025. To appear. |
| [13] |
S. Hirt, A. Höhl, J. Pohlodek, J. Schaeffer, M. Pfefferkorn, R. D. Braatz, R. Findeisen: Safe Learning-Based Optimization of Model Predictive Control: Application to Battery Fast-Charging. American Control Conference, 2025. To appear. |
| 2024 | |
| [12] |
M. Pfefferkorn, R. Findeisen: Probabilistically Input-to-State Stable Stochastic Model Predictive Control. Conference on Decision and Control, pages 1807 – 1813, 2024. |
| [11] |
S. Hirt, M. Pfefferkorn, R. Findeisen: Safe and Stable Closed-Loop Learning for Neural-Network-Supported Model Predictive Control. Conference on Decision and Control, pages 4764 – 4770, 2024. |
| [10] |
S. Hirt, M. Pfefferkorn, A. Mesbah, R. Findeisen: Stability-informed Bayesian Optimization for MPC Cost Function Learning. IFAC-PapersOnLine 58 (18), pages 208 – 213, 2024. |
| [9] |
M. Pfefferkorn, V. Renganathan, R. Findeisen: Regret and Conservatism of Distributionally Robust Constrained Stochastic Model Predictive Control. American Control Conference, pages 3251 – 3257, 2024. |
| [8] |
P. Holzmann, M. Pfefferkorn, J. Peters, R. Findeisen: Learning Energy-Efficient Trajectory Planning for Robotic Manipulators using Bayesian Optimization. European Control Conference, pages 1374 – 1379, 2024. |
| 2023 | |
| [7] |
A. Rose, M. Pfefferkorn, H. H. Nguyen, R. Findeisen: Learning a Gaussian Process Approximation of a Model Predictive Controller with Gurantees. Conference on Decision and Control, pages 4094 – 4099, 2023. |
| [6] |
J. Bethge, M. Pfefferkorn, A. Rose, J. Peters, R. Findeisen: Model Predictive Control with Gaussian-Process-Supported Dynamical Constraints for Autonomous Vehicles. IFAC-PapersOnline 56 (2), pages 507 – 512, 2023. |
| 2022 | |
| [5] |
M. Pfefferkorn, P. Holzmann, J. Matschek, R. Findeisen: Safe Corridor Learning for Model Predictive Path Following Control. IFAC-PapersOnline 55 (30), pages 79 – 84, 2022. |
| [4] |
H. H. Nguyen, M. Pfefferkorn, R. Findeisen: High-probability stable Gaussian-process-supported model predictive control for Lur'e systems. European Journal of Control 68, 100695, 2022. |
| [3] |
P. Holzmann, J. Matschek, M. Pfefferkorn, R. Findeisen: Learning secure corridors for model predictive path following control of autonomous systems in cluttered environments. European Control Conference, pages 1772 – 1777, 2022. |
| [2] |
M. Pfefferkorn, M. Maiworm, R. Findeisen: Exact Multiple-Step Predictions in Gaussian-Process-based Model Predictive Control: Observations, Possibilities, and Challenges. American Control Conference, pages 2829 – 2836, 2022. |
| 2020 | |
| [1] |
M. Pfefferkorn, M. Maiworm, C. Wagner, F. S. Tautz, R. Findeisen: Fusing Online Gaussian-Process-Based Learning and Control for Scanning Quantum Dot Microscopy. Conference on Decision and Control, pages 5525 – 5531, 2020. |
| since 06/2023 | Research assistant and Ph.D. student at the Control and Cyber-Physical Systems Laboratory (Prof. Rolf Findeisen), Technical University of Darmstadt (Germany) |
| 06/2020 – 05/2024 | Regular fellow of the graduate program Mathematical Complexity Reduction (DFG GRK 2297) at the Faculty of Mathematics, Otto-von-Guericke University Magdeburg |
| 03/2020 – 05/2024 | Research assistant and Ph.D. student at the Systems Theory and Automatic Control Laboratory (Prof. Rolf Findeisen), Otto-von-Guericke University Magdeburg (Germany) |
| 01/2020 | Master's degree (M.Sc.) in Biosystems Engineering from the Otto-von-Guericke University Magdeburg (Germany) |
| 05/2018 | Bachelor's degree (B.Sc.) in Biosystems Engineering from the Otto-von-Guericke University Magdeburg (Germany) |