The Ohio State University

Abstract:

Model Predictive Control (MPC) is a promising approach for high performance multivariable control applications, since it systematically handles constraints on system inputs, states, and outputs, and shapes transient response through the optimization of a user-defined performance criterion. Even though MPC can be applied to any type of dynamics, efficient computational algorithms have been proposed for MPC of linear systems and of hybrid dynamical systems. The use of multiparametric programming allows to synthesize the MPC controller in the form of a piecewise linear state-feedback. Thus, MPC that was historically limited to controlling slow dynamics with abundant computing resources, such as in the chemical process industry, can now be applied to fast processes with limited computing power, for instance, the ones in automotive applications.

In this talk we discuss recent results in linear and hybrid MPC, including stabilization techniques, controller matching designs, stochastic MPC, and (wireless) networked MPC. We also present applications of linear, switched, and hybrid MPC in different domains. In particular, we describe the application of linear and switched model predictive control to idle speed regulation developed at Ford Motor Company, and the network hybrid MPC strategy applied for controlling a process where feedback measurements are obtained via a wireless T-Motes sensor network. For these applications, experimental results are reported. 

Short Bio:

Dr Stefano Di Cairano received the Master degree (Laurea) in Computer Engineering in 2004, and the PhD degree in Information Engineering - Automatic Control area, in 2008, both from the University of Siena, Italy. He was granted the International Curriculum Option of Doctoral Studies in "Hybrid Control for Complex, Distributed and Heterogeneous Embedded Systems" in 2008. During doctoral studies, his main research areas were Hybrid Dynamical Systems and Hybrid Model Predictive Control, and their application to Networked Control Systems. 

He was visiting student at the Information and Mathematical Modeling Dept., the Technical University of Denmark, in 2002-2003. He was visiting researcher at the Control and Dynamical Systems Dept., the California Institute of Technology, in 2006-2007, where he participated with Team Caltech in the 2007 DARPA Urban Challenge. In April 2008, he joined the Powertrain control R&A dept, at Ford Motor Company, Dearborn, MI, where he is investing and developing model predictive control applications to powertrain, vehicle dynamics, and production planning.

His research interests also include optimization algorithms, stochastic systems, and distributed control and estimation algorithms.

Hosted by Professor Giorgio Rizzoni in E001 Scott Laboratory.