ETH Zürich, Professor
Data enabled predictive control
Model predictive control (MPC) calls for repeatedly solving an optimisation problem on-line and applying the “opening moves” of the optimal decision to the system in receding horizon fashion. Though computationally demanding at first sight, with advances in embedded computation and optimisation MPC, has emerged as a powerful methodology for a range of applications, fast and slow. In many of these applications, however, obtaining a model of the system dynamics, the “M” in MPC, to include in the constraints of the optimisation problem can be challenging. The standard approach is to use data collected from the system in a two step process of system identification to get an “M”, followed by conventional “PC”. Here we explore an alternative one step approach, where the data is used directly in the constraints of the optimisation problem. We show that for deterministic linear systems this is equivalent to conventional MPC. The method is then extended to uncertain or nonlinear systems through regularisation; we discuss how this can be interpreted as robustifying the optimisation problem against uncertainty in the data. Finally, we demonstrate the applicability of the method through benchmark examples and problems in power systems.
Biography of the speaker
John Lygeros received a B.Eng. degree in 1990 and an M.Sc. degree in 1991 from Imperial College, London, U.K. and a Ph.D. degree in 1996 at the University of California, Berkeley. After research appointments at M.I.T., U.C. Berkeley and SRI International, he joined the University of Cambridge in 2000 as a University Lecturer. Between March 2003 and July 2006 he was an Assistant Professor at the Department of Electrical and Computer Engineering, University of Patras, Greece. In July 2006 he joined the Automatic Control Laboratory at ETH Zurich where he is currently serving as the Professor for Computation and Control and the Head of the laboratory. His research interests include modelling, analysis, and control of large scale systems, with applications to biochemical networks, energy systems, transportation, and industrial processes. John Lygeros is a Fellow of IEEE, and a member of IET and the Technical Chamber of Greece. Since 2013 he is serving as the Vice-President Finances and a Council Member of the International Federation of Automatic Control and since 2020 as the Director of the National Center of Competence in Research “Dependable Ubiquitous Automation” (NCCR Automation).