Stefan Ecklebe

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© Stefan Ecklebe

Name

Mr Dipl.-Ing. Stefan Ecklebe

Research Fellow

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Contact Information

Organization Name

Institute of Control Theory

Institute of Control Theory

Address work

Visit address:

Institutsgebäude S7a, 4th Floor, Room 408 Georg-Schumann-Straße 7a

01187 Dresden

Germany

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work Tel.

+49 351 463-34311

fax Fax

+49 351 463-37281

stefan.ecklebe@​tu-dresden.de

Field of activity

• Work in DFG-Project "STABEEL - Development of criteria for the robust proof of plant stability for the provision of system services by a large number of decentralized generation units"

• Model predictive control of nonlinear distributed parameter systems

• Data-driven modelling of crystal growth processes

Completed projects

• DFG-Projekt WI 4412/1-1 "Model based control of the Vertical Gradient Freeze crystal growth process with distributed paramter methods"

Software projects

• pyinduct: Simulation and control design for distributed parameter systems
• pymoskito: Integrated development environment for block based control system synthesis

Publications

As author

Toward Model-Based Control of the Vertical Gradient Freeze Crystal Growth Process February 2021, IEEE Transactions on Control Systems Technology PP(99):1-8 DOI: 10.1109/TCST.2021.3058006

Control of the Vertical Gradient Freeze crystal growth process via backstepping, July 2021, IFAC-PapersOnLine DOI: t.b.a.

Approximation and implementation of transformation based feedback laws for distributed parameter systems December 2017PAMM 17(1):785-786 DOI: 10.1002/pamm.201710360

As co-author

Real Time Predictions of VGF-GaAs Growth Dynamics by LSTM Neural Networks January 2021, Crystals 11(2):138 DOI: 10.3390/cryst11020138   

Extraction of Classification Rules from Sequences of Crystal Growth Data September 2020, Conference: WCIDM ITAT 2020 At: Oravská Lesná, Slovakia Volume: Proceedings of the 20th Conference Information Technologies - Applications and Theory (ITAT 2020), Computational Intelligence and Data Mining - 8th International Workshop (CIDM 2020), Oravská Lesná, 18-22.September, 2020, pp. 99-105. https://ics.upjs.sk/~horvath/itat2020/ 

Fast forecasting of VGF crystal growth process by dynamic neural networks May 2019, Journal of Crystal Growth 521:9-14 DOI: 10.1016/j.jcrysgro.2019.05.022

On approximation and implementation of transformation based feedback laws for distributed parameter systems July 2017, IFAC-PapersOnLine 50(1):6786-6792 DOI: 10.1016/j.ifacol.2017.08.1197

Academic CV