SOURCE-LINKED INTELLIGENCE
Robust and data-Efficient Learning for Industrial Control
on my industrial experience to develop operating strategies for distribution networks that will enable safe implementation and reaching the environmental targets. There is a potential in integrating machine learning in control design to overcome the complexity while satisfying safety constraints, as shown in robotics and automotive industry. However, IPCC indicated that ""The key challenge for making an assessment of the industry sector is the diversity in practices, which results in uncertainty, lack of comparability, incompleteness, and quality of data available in the public domain on process and technology specific energy use and costs"". The research question I will address in this project is if and how incorporating data-driven learning in design of control algorithms leads to improved environmental performance and safe operation of large-scale industrial networks." Model predictive control, Robust control, Optimisation under uncertainty, Gas transport
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- recordType
- award
- status
- TERMINATED
- region
- EU
- value
- 210911.04
- unit
- EUR
Evidence & attribution
European Commission, CORDIS Horizon Europe project dataset. Metadata adapted.
License: CORDIS reuse policy
First collected: 2026-09-20T00:21:03.701Z. This is not the publication date.