zurück zur Liste

P

Johannes Pohlodek

Arbeitsgebiet(e)

Machine learning and estimation; Process engineering of biotechnological processes; Model-based concepts for process control, optimization and analysis

Kontakt

work +49 6151 16-25177

Work S3|10 518
Landgraf-Georg-Str. 4
64283 Darmstadt

Datengetriebene Modellierung dynamischer Systeme

Modellbildung, Simulation und Optimierung

Control of Distributed Cyber-Physical Systems

Thema Typ Status
Two-degrees-of-freedom (2DOF) Controller for Speed Regulation of a DC Motor (wird in neuem Tab geöffnet) Bachelorarbeit vergeben
Sparse Identification of Nonlinear Dynamics for Model Predictive Control (wird in neuem Tab geöffnet) (Co-Betreuer: Rudolph Kok) Masterarbeit abgeschlossen
Gaussian Process Regression for Big Data (wird in neuem Tab geöffnet) (Co-Betreuer: Alexander Rose) Masterarbeit abgeschlossen
Bayesian Optimization of Battery Fast-charging Protocols (wird in neuem Tab geöffnet) (Co-Betreuer: Joachim Schaeffer) Masterarbeit abgeschlossen
Catching Objects with a Robot Arm (wird in neuem Tab geöffnet) (Co-Betreuer: Alexander Rose, Philipp Holzmann) Projektseminar abgeschlossen
Reinforcement Learning for Control of Bioreactors (wird in neuem Tab geöffnet)
(Co-Betreuer: Sebstián Espinel Ríos) 		Masterarbeit abgeschlossen
Control of a Partial Differential Equation System Using Physics-informed Neural Networks (wird in neuem Tab geöffnet) (Co-Betreuer: Rudolph Kok) Masterarbeit abgeschlossen
Physics-informed Training of Neural Networks for Control of a Bioreactor (wird in neuem Tab geöffnet) (Co-Betreuer: Sebstián Espinel Ríos) Masterarbeit abgeschlossen
Control of a Partial Differential Equation System Using Recurrent Neural Networks (wird in neuem Tab geöffnet) (Co-Betreuer: Rudolph Kok) Projektseminar abgeschlossen
Autotuning eines modellbasierten Zwei-Freiheitsgrade-Reglers (2DOF) (wird in neuem Tab geöffnet) (Co-Betreuerin: Lena Kranert) Projektseminar abgeschlossen
Zeitschriftenartikel
[4] L. Kranert, J. Pohlodek, S. Duvigneau, A. Rose, L. Carius, A. Kienle, and R. Findeisen, „Step experiments enable efficient exploration of microbial microaerobic steady states,“ submitted. Authorea: 10.22541/au.167700378.88413405/v1
[3] S. Espinel-Ríos, G. Behrendt, J. Bauer, B. Morabito, J. Pohlodek, A. Schütze, R. Findeisen, K. Bettenbrock, S. Klamt, „Experimentally implemented dynamic optogenetic optimization of ATPase expression using knowledge-based and Gaussian-process-supported models,“ Process Biochemistry, vol. 143, pp. 174–185, 2024. doi: 10.1016/j.procbio.2024.04.032
[2] 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,“ International Journal of Robust and Nonlinear Control, 2024. doi: 10.1002/rnc.7275
[1] S. Espinel-Ríos, B. Morabito, J. Pohlodek, K. Bettenbrock, S. Klamt, and R. Findeisen, „Toward a modeling, optimization, and predictive control framework for fed-batch metabolic cybergenetics,“ Biotechnology and Bioengineering, vol. 121, no. 1, pp. 366–379, 2024. doi: 10.1002/bit.28575
Konferenzbeiträge
[8] S. Hirt, A. Höhl, J. Pohlodek, J. Schaeffer, M. Pfefferkorn, R. D. Braatz, and R. Findeisen, „Safe Learning-Based Optimization of Model Predictive Control: Application to Battery Fast-Charging,“ submitted.
[7] S. Hirt, A. Höhl, J. Schaeffer, J. Pohlodek, R. D. Braatz, and R. Findeisen, „Learning Model Predictive Control Parameters via Bayesian Optimization for Battery Fast Charging,“ IFAC-PapersOnLine, vol. 58, no. 14, pp. 742–747, 2024, 12th IFAC Symposium on Advanced Control of Chemical Processes ADCHEM 2024. doi: 10.1016/j.ifacol.2024.08.426
[6] J. Pohlodek, H. Alsmeier, B. Morabito, C. Schlauch, A. Savchenko, and R. Findeisen, „Stochastic Model Predictive Control Utilizing Bayesian Neural Networks,“ in 2023 American Control Conference (ACC). IEEE, 2023, pp. 603–608. doi: 10.23919/ACC55779.2023.10156115
[5] S. Espinel-Ríos, 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, vol. 55, no. 7, pp. 174–180, 2022, 13th IFAC Symposium on Dynamics and Control of Process Systems, including Biosystems DYCOPS 2022. doi: 10.1016/j.ifacol.2022.07.440
[4] B. Morabito, J. Pohlodek, L. Kranert, S. Espinel-Ríos, and R. Findeisen, „Efficient and Simple Gaussian Process Supported Stochastic Model Predictive Control for Bioreactors using HILO-MPC,“ IFAC-PapersOnLine, vol. 55, no. 7, pp. 922–927, 2022, 13th IFAC Symposium on Dynamics and Control of Process Systems, including Biosystems DYCOPS 2022. doi: 10.1016/j.ifacol.2022.07.562
[3] 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, vol. 54, no. 6, pp. 321–328, 2021, 7th IFAC Conference on Nonlinear Model Predictive Control NMPC 2021. doi: 10.1016/j.ifacol.2021.08.564
[2] J. Pohlodek, A. Rose, B. Morabito, L. Carius, and R. Findeisen, „Data-driven Metabolic Network Reduction for Multiple Modes Considering Uncertain Measurements,“ IFAC-PapersOnLine, vol. 53, no. 2, pp. 16866–16871, 2020, 21st IFAC World Congress. doi: 10.1016/j.ifacol.2020.12.1215
[1] L. Carius, J. Pohlodek, B. Morabito, A. Franz, M. Mangold, R. Findeisen, and A. Kienle, „Model-based State Estimation Based on Hybrid Cybernetic Models,“ IFAC-PapersOnLine, vol. 51, no. 18, pp. 197–202, 2018, 10th IFAC Symposium on Advanced Control of Chemical Processes ADCHEM 2018. doi: 10.1016/j.ifacol.2018.09.299