Institute for production technology and systems
Organisational unit: Institute
- Professorship for Engineering, in particular Modelling and Simulation of Technical Systems and Processes
- Professorship for Machine Tools
- Professorship for Manufacturing – Innovative Manufacturing
- Professorship for materials mechanics
- Professorship for Materials Technology, in particular of Magnesium Materials
- Professorship for Process Measurement Technology and Intelligent Systems
- Professorship for Product Development and Design
- Professorship of Control and Drive Systems
- Professorship of Production Management
Organisation profile
The members of the Institute for Production Technology and Systems (IPTS) carry out work on a wide range of engineering-related research questions.
When it comes to subjects, the members of the Institute concentrate on the topics of supply chain management and production management, as well as on measurement, control, plasma, production and material technology. Within these fields, we aim to answer current research questions through both experimental work and the use of digital modelling. Our overarching goal here is to provide better solution strategies for complex problems within the field of engineering.
- Journal articles › Research › Peer-reviewed
- Published
Short-arc measurement and fitting based on the bidirectional prediction of observed data
Fei, Z., Xu, X. & Georgiadis, A., 05.01.2016, In: Measurement Science and Technology. 27, 2, 19 p., 025013.Research output: Journal contributions › Journal articles › Research › peer-review
- Published
Simple relay non-linear PD control for faster and high-precision motion systems with friction
Zheng, C., Su, Y. & Mercorelli, P., 27.11.2018, In: IET Control Theory and Applications. 12, 17, p. 2302-2308 7 p.Research output: Journal contributions › Journal articles › Research › peer-review
- Published
Simple saturated relay non-linear PD control for uncertain motion systems with friction and actuator constraint
Zheng, C., Su, Y. & Mercorelli, P., 13.08.2019, In: IET Control Theory and Applications. 13, 12, p. 1920-1928 9 p.Research output: Journal contributions › Journal articles › Research › peer-review
- Published
Single Robust Proportional-Derivative Control for Friction Compensation in Fast and Precise Motion Systems With Actuator Constraint
Zheng, C., Su, Y. & Mercorelli, P., 01.11.2020, In: Journal of Dynamic Systems, Measurement and Control. 142, 11, 10 p., 114505.Research output: Journal contributions › Journal articles › Research › peer-review
- Published
Sliding mode and model predictive control for inverse pendulum
Schwab, K. C., Schräder, L., Mercorelli, P. & Lassen, J. T., 01.01.2019, In: WSEAS Transactions on Systems and Control. 14, p. 190-195 6 p., 23.Research output: Journal contributions › Journal articles › Research › peer-review
- Published
Sliding Mode Control for a Vertical Dynamics in the Presence of Nonlinear Friction
Ferch, T. & Mercorelli, P., 2019, In: WSEAS Transactions on Circuits and Systems. 18, p. 102-112 11 p., 17.Research output: Journal contributions › Journal articles › Research › peer-review
- Published
Smarte Anpassung von Presslinienparametern: Bildgebende Sensorik und maschinelles Lernen für robustere Blechumformprozesse im Automobilbau
Heger, J., Voß, T. & Selent, M., 04.2018, In: Industrie 4.0 Management. 34, 4, p. 53-56 4 p.Research output: Journal contributions › Journal articles › Research › peer-review
- Published
SPS steuern Assistenzsysteme in der Digitalen Fabrik: Integration eines Laser-Assistenzsystems zur Werkerführung in die Steuerungsebene der Digitalen Fabrik
Müller-Polyzou, R., Meier, N., Berwanger, F. & Georgiadis, A., 13.08.2019, In: Industrie 4.0 Management. 35, 4, p. 13-16 4 p.Research output: Journal contributions › Journal articles › Research › peer-review
- Published
Stability analysis of a linear model predictive control and its application in a water recovery process
Mercorelli, P., 01.01.2019, In: Advances in Science, Technology and Engineering Systems. 4, 5, p. 314-320 7 p.Research output: Journal contributions › Journal articles › Research › peer-review
- Published
Strategisches Supply-Chain-Risikomanagement: Einsatz von Künstlicher Intelligenz und Big Data zur Unterstützung des strategischen Supply-Chain-Risikomanagements
Kramer, K., Mousavi, D. & Schmidt, M., 30.05.2022, In: ZWF Zeitschrift für wirtschaftlichen Fabrikbetrieb. 117, 5, p. 349-353 5 p.Research output: Journal contributions › Journal articles › Research › peer-review