Special Session 10

SPECIAL SESSION #10

Mattia Bruschetta

University of Padova, Italy

• mattia.bruschetta@unipd.it

The use of advanced control techniques in modern vehicles is now widespread, made possible by the increased availability of computing power in embedded chips and the maturing of real-time and data-driven methodologies. Indeed, there is a growing number of studies that demonstrate the ability to estimate quantities previously only measurable with costly sensors, making these quantities available for advanced control applications. An example of this is the twin-in-the-loop approach to side slip estimation. This trend allows for the design and implementation of more cost-effective and less accurate sensors, which in combination with advanced techniques can result in reliable estimates of complex quantities.

This special session focuses on the development and implementation of advanced control and estimation strategies addressing sensor critical scenario. Submissions are encouraged on a wide spectrum of topics including (but not limited to):

• Optimization based estimation of hard-to-measure vehicle quantities;
• Optimization based control relying on uncertain/inaccurate measurements (robust strategies);
• Twin-in-the-loop estimation and control implementation;
• Learning based soft-sensors;
• Monitoring based on on-board digital twin (virtual or real)

Mattia Bruschetta received the M.Sc. degrees in automation engineering and the Ph.D. degree in 2007, and 2011, respectively from Department of Information Engineering, University of Padova, Italy, where he is currently a Assistant Professor. He has authored or co-authored over 50 papers in international journals or proceedings of conferences. His current research interests include model predictive control, automotive, virtual prototyping, motion cueing, virtual driver/rider, system identification, and numerical integration, with specific focus on automotive applications.