MBSE Colloquium: Sasa Rakovic, "Robust Model Predictive Control"
Monday, February 22, 2016
2168 AV Williams Bldg
Model-Based Systems Engineering Colloquia Series
Robust Model Predictive Control
Centre for Space Research
University of Texas
Model predictive control (MPC) is an advanced control technique that employs an open-loop online optimization in order to take account of system dynamics, constraints and control objectives and to obtain the best current control action. Robust MPC (RMPC) is an improved MPC form that is robust against the bounded uncertainty. RMPC employs a generalized prediction framework that allows for a meaningful optimization of, and over, the set of possible system behaviours effected by the uncertainty.
The seminar focuses on an overview of RMPC methods with particular emphasis on tube MPC (TMPC) synthesis methods developed through my research investigations and collaborations. Tube MPC has emerged as a dominant framework for RMPC, since it is theoretically sound and computationally efficient. A survey of main contributions to RMPC is complemented with historical remarks of the related developments as well as personal view of future developments in MPC under uncertainty.
Sasa V. Rakovic received the PhD degree in Control Theory from Imperial College London. His PhD thesis, entitled "Robust Control of Constrained Discrete Time Systems: Characterization and Implementation," was awarded the Eryl Cadwaladr Davies Prize as the best PhD thesis in the Department of Electrical and Electronic Engineering at Imperial College London in 2005.
Sasa V. Rakovic was affiliated with a number of the well-known universities, including Imperial College London, ETH Zurich, Oxford University and the University of Maryland at College Park. He is currently a Visiting Scholar with the Centre for Space Research of the University of Texas at Austin.
Sasa V. Rakovic's main interests and contributions lie within the areas of synthesis of control systems, analysis of dynamical systems and decision making under constraints and uncertainty.