Alberto Bemporad
Professor of Control Systems,
IMT School for Advanced Studies Lucca
IAS SEMINAR #02
Turin / OGR / Speaker's Corner / 16 September 2025 / 02:00-05:00 PM CEST
Efficient learning algorithms for model predictive control
Alberto Bemporad
EVENT&WEBINAR
Machine learning has gained immense popularity in many fields, including control, thanks to its ability to extract mathematical models directly from data. In this talk, I will present a range of learning algorithms that support the design of model predictive control (MPC) laws. The focus will be on batch and incremental methods for identifying nonlinear state-space models, such as recurrent neural networks, potentially under nonsmooth L1 and group-Lasso penalties. These methods, belonging to the family of quasi-Newton optimization algorithms, combine high accuracy with computational efficiency and often outperform classical gradient-descent approaches in control-oriented model-learning tasks. I will illustrate their effectiveness in learning nonlinear prediction models from data and in deriving convex parametric functions for MPC design, and show how incremental learning can be applied to adapt disturbance models online, enabling offset-free tracking in nonlinear MPC.
Alberto Bemporad
Alberto Bemporad received his Master’s degree cum laude in Electrical Engineering in 1993 and his Ph.D. in Control Engineering in 1997 from the University of Florence, Italy. In 1996/97 he was with the Center for Robotics and Automation, Department of Systems Science & Mathematics, Washington University, St. Louis.
In 1997-1999 he held a postdoctoral position at the Automatic Control Laboratory, ETH Zurich, Switzerland, where he collaborated as a Senior Researcher until 2002. In 1999-2009 he was with the Department of Information Engineering of the University of Siena, Italy, becoming an Associate Professor in 2005. In 2010-2011 he was with the Department of Mechanical and Structural Engineering of the University of Trento, Italy. Since 2011 he has been a Full Professor at the IMT School for Advanced Studies Lucca, Italy, where he served as the Director of the institute from 2012 to 2015.
He spent visiting periods at Stanford University, the University of Michigan, and Zhejiang University. In 2011 he co-founded ODYS S.r.l., a company specialized in developing model predictive control systems for industrial production. He has published more than 400 papers in the areas of model predictive control, hybrid systems, optimization, and automotive control, and is the co-inventor of 21 patents. He is the author or coauthor of various software packages for model predictive control design and implementation, including the Model Predictive Control Toolbox (The Mathworks, Inc.) and the Hybrid Toolbox for MATLAB. He was an Associate Editor of the IEEE Transactions on Automatic Control during 2001-2004 and Chair of the Technical Committee on Hybrid Systems of the IEEE Control Systems Society from 2002 to 2010. He received the IFAC High-Impact Paper Award for the 2011-14 triennial, the IEEE CSS Transition to Practice Award in 2019, the 2021 SAE Environmental Excellence in Transportation Award, the 2024 Beale–Orchard-Hays Prize for Excellence in Computational Mathematical Programming, and an ERC Advanced Research Grant in 2024. He is an IEEE Fellow since 2010 and an IFAC Fellow since 2025.
