TITLE: Recent Advances in Model Predictive Control
DATE: Wednesday, 24 February 2016
PLACE: SUPSI-DTI, Galleria 2, Sala Primavera
Model Predictive Control (MPC) is one of the most successful techniques adopted in industry to control multivariable systems in an optimized way under constraints on input and output variables. In MPC, the manipulated inputs are computed in real-time by solving a mathematical programming problem, most frequently a Quadratic Program (QP). The QP depends on a model of the dynamics of the system, that is often learned from experimental data. To adopt MPC in embedded control systems under fast sampling and limited CPU and memory resources, one must be able to solve QP's with high throughput, using simple code and executing arithmetic operations under limited machine precision, and to provide tight estimates of execution time. In my talk I will present recent advances in system identification and embedded quadratic optimization for MPC, also showing numerical evidence obtained on embedded control hardware platforms.
Alberto Bemporad received his master's degree 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. In 2011 he became full professor at the IMT Institute for Advanced Studies Lucca, Italy, serving as director of the institute in 2012-2015. In 2011 he cofounded ODYS S.r.l, a consulting and software development company specialized in advanced controls and embedded optimization algortithms. He has published more than 300 papers in the areas of model predictive control, automotive control, hybrid systems, multiparametric optimization, computational geometry, robotics, and finance. He is author or coauthor of various MATLAB toolboxes for model predictive control design, including the Model Predictive Control Toolbox (The Mathworks, Inc.). 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 in 2002-2010. He received the IFAC High-Impact Paper Award for the 2011-14 triennial. He has been an IEEE Fellow since 2010.