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Exploiting previewed information in estimation and control

Information preview can be encountered in numerous control (e.g., robotics and automotive control) and signal processing applications and is a factor that may potentially improve control/estimation performance. We appreciate this frequently when slowing down after seeing a speed bump ahead while driving. In systems with complicated dynamics the question of how to exploit preview is far less trivial than the mere “slowing down” and the current study concerns with development of strategies for utilizing the available preview. This talk addresses the general model matching setup, which can be considered as a unified framework for both control and estimation problems with preview. The one-side special case of model matching with preview is currently well understood and the present work concerns with a nontrivial extension of existing one-side methods to the general two-side setting. I will present explicit and numerically efficient solutions to the stabilization and H2 optimization problems, which can be associated with asymptotic and dynamic behavior of the underlying control/ estimation system, respectively. The potential of these theoretical results will be demonstrated by two case studies and laboratory experiments.

Type of Seminar:
IfA Seminar
Dr. Maxim Kristalny
Department of Automatic Control, Lund University, Sweden
Oct 11, 2010   15.15

Contact Person:

Manfred Morari
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Biographical Sketch:
Maxim Kristalny was born in Saint Petersburg, USSR (Russia) in 1979. He received the B.Sc. in Mechanical Engineering from the Technion - Israel Institute of Technology, Haifa, Israel in 2001. In 2005 he returned to the Faculty of Mechanical Engineering in the Technion - Israel Institute of Technology as a graduate student on a direct Ph.D. track. He received the Ph.D. degree in August 2010 and from September 2010 is a postdoc at the Department of Automatic Control in Lund University, Sweden. His research interests include systems theory, control and estimation problems with information preview, and problems with asymptotic behavior constraints.