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Johannes Pohlodek

Working area(s)

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

Contact

work +49 6151 16-25177

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

Data-driven Modeling of Dynamic Systems

Modeling, Simulation and Optimization

Control of Distributed Cyber-Physical Systems

Topic Type Status
Two-degrees-of-freedom (2DOF) Controller for Speed Regulation of a DC Motor (opens in new tab) Bachelor's Thesis open
Split-range Control for Applications with 2 Manipulated Control Variables (opens in new tab) Bachelor's Thesis open
Gaussian Process Regression for Big Data (opens in new tab) Master's Thesis in progress
Sparse Identification of Nonlinear Dynamics for Model Predictive Control (opens in new tab) (Co-Supervisor: Rudolph Kok) Master's Thesis in progress
Bayesian Optimization of Battery Fast-charging Protocols (opens in new tab) (Co-Supervisor: Joachim Schaeffer) Master's Thesis completed
Catching Objects with a Robot Arm (opens in new tab) (Co-Supervisors: Alexander Rose, Philipp Holzmann) Project Course completed
Reinforcement Learning for Control of Bioreactors (opens in new tab)
(Co-Supervisor: Sebstián Espinel Ríos) 		Master's Thesis completed
Control of a Partial Differential Equation System Using Physics-informed Neural Networks (opens in new tab) (Co-Supervisor: Rudolph Kok) Master's Thesis completed
Physics-informed Training of Neural Networks for Control of a Bioreactor (opens in new tab) (Co-Supervisor: Sebstián Espinel Ríos) Master's Thesis completed
Control of a Partial Differential Equation System Using Recurrent Neural Networks (opens in new tab) (Co-Supervisor: Rudolph Kok) Project Course completed
Autotuning eines modellbasierten Zwei-Freiheitsgrade-Reglers (2DOF) (opens in new tab) (Co-Supervisor: Lena Kranert) Project Course completed
Journal Articles
[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
Conference Proceedings
[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,” 2024, 12th IFAC Symposium on Advanced Control of Chemical Processes ADCHEM 2024, accepted.
[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