ABSTRACT: The use of data-driven and learning-based models in model predictive control (MPC) have gained an increasing popularity in recent years thanks to the growing availability of data collected in industrial plants and on the development of powerful deep learning techniques. In this framework, the aim of the talk is to describe how to to design nonlinear MPC algorithms with guaranteed stability and robustness properties based on data-driven models of the system under control. A particular attention is devoted to the development of strategies that take into account modeling errors and uncertainties. In particular, the design of MPC algorithms for incrementally input-to-state stable (δISS) nonlinear systems modeled by recurrent neural networks (RNN) is considered. This class of models can be trained using input-output data, and stability properties of the model can be enforced during the training procedure. Considering different RNN architectures, during the talk output-feedback MPC algorithms that ensure closed-loop stability, satisfaction of input and incremental input constraints, robust satisfaction of output constraints in presence of uncertainties and offset-free tracking are presented. Moreover, considering a general δISS system, it is presented how it is possible to guarantee stability considering a positive semi-definite stage cost in the MPC optimization problem (e.g. for output weighting), and how it is possible to enlarge the feasibility region employing an artificial reference.

BIOSKETCH: Lalo Magni is Full Professor of Automatic Control at the University of Pavia. From October 2016 to September 2022 was Dean of Engineering Faculty. He spent several months at CESAME, Universitè Catholique de Louvain, Louvain La Neuve (Belgium) and at University of Twente (The Netherlands) with the System and Control Group in the Faculty of Applied Mathematics. He was plenary, semi-plenary or Keynote speaker at several IFAC Workshops and Conferences. He was Guest Editor of International Journal of Robust and Nonlinear Control and he served as an Associate Editor of the IEEE Transactions on Automatic Control and of Automatica. He was Chair of the NMPC Workshop on Assessment and Future Direction, September 5-9, 2008 Pavia, Italy. He has several research collaborations with companies. He was the national principal investigator and the local principal investigator of UE and Italian projects.

His current research interests include nonlinear control, predictive control, robust control, process control and artificial pancreas. His research is witnessed by more than 100 papers published in the main international journals (8482 citations, H-index 48, Scopus).