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Minimax MPC using semidefinite programming

One of the most popular approaches to incorporate uncertainty in model predictive control (MPC) is to solve minimax problems, i.e., optimize worst-case performance. Unfortunately, many problems tend to be intractable, in particular formulations with the standard quadratic performance measure. The talk focuses on semidefinite programming as a tool to solve conservative approximations of various minimax MPC problems. Models with bounded external disturbances and models with parametric uncertainty are addressed. If time permits, extensions to closed-loop minimax schemes and incorporation of state estimation will be discussed.
Type of Seminar:
Public Seminar
Johan Löfberg, MSc., Lic. Eng.
Division of Automatic Control Department of Electrical Engineering Linköpings universitet
Feb 27, 2003   14:00

ETH Zentrum, Gloriastrasse 35, 8006 Zurich, Building ETZ, Room E7
Contact Person:

Prof. P. Parrilo
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