Alberto Bemporad, IMT School for Advanced Studies Lucca, will give the above talk on:
28.10.2026 (Wednesday)
16:00 – 17:00 (including discussion)
Building S3|11 Room 006 Hexagon and via Zoom
Abstract
In this talk, I will review several recently developed learning-based methods for model predictive control (MPC), with emphasis on their use in designing nonlinear and game-theoretic MPC laws from data. I will cover learning nonlinear prediction models from data, learning the MPC cost function from samples of the function, of actions, or of user preferences, and learning to solve MPC problems efficiently to drastically reduce online computation time.
Short CV
Alberto Bemporad received his M.S. degree cum laude in Electrical Engineering (1993) and his Ph.D. in Control Engineering (1997) from the University of Florence, Italy. He held research positions at Washington University in St. Louis (1996-1997) and ETH Zurich (1997-2002). He was a faculty member at the University of Siena (1999-2009) and at the University of Trento (2010-2011). Since 2011, he has been Full Professor at the IMT School for Advanced Studies Lucca, where he was Director from 2012 to 2015. In 2011, he co-founded ODYS S.r.l., a company specializing in industrial model predictive control systems, and he has held visiting appointments at Stanford University, the University of Michigan, and Zhejiang University. He has authored over 400 publications and 21 patents in model predictive control, numerical optimization, nonlinear system identification, and automotive control, and co-developed the Model Predictive Control Toolbox for MATLAB (The MathWorks). He was an Associate Editor of the IEEE Transactions on Automatic Control (2001-2004) and Chair of the IEEE CSS Technical Committee on Hybrid Systems (2002-2010). He is an IEEE Fellow (since 2010) and an IFAC Fellow (since 2025). His awards include the IFAC High-Impact Paper Award (2011-2014), the IEEE CSS Transition to Practice Award (2019), the SAE Environmental Excellence in Transportation Award (2021), the Beale-Orchard-Hays Prize (2024), and an ERC Advanced Grant (2024).
Guests via Zoom are welcome:
Meeting Link: https://tu-darmstadt.zoom.us/j/65324628051?pwd=ZXQyZk1EYkExa01BMlVMU2hPRjRUUT09
Meeting ID: 653 2462 8051
Passcode: 569811
