Implementing model-based control systems in industrial applications presents two significant challenges: developing accurate yet robust mathematical models and calibrating controller parameters to ensure optimal performance, not only in ideal conditions but throughout the product’s lifecycle. These processes are often time-intensive, technically demanding, and costly.
To overcome these challenges, the new project SLIMPEC will create advanced software tools that leverage machine learning and control system theory. These tools aim to:
- Construct physics-informed predictive models that seamlessly integrate white-box process knowledge with data-driven learning and adaptive techniques, and
- Establish (semi-)automated calibration methods to deliver near-optimal parameter tuning offline while enabling real-time adjustments to adapt to evolving operating conditions.
The project, kicked-off on November 1st, 2023, and expected to complete by October 31st, 2026, is carried out by ODYS and SUPSI within the Eureka Eurostars programme of Horizon Europe.
