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SUMMARY:Model predictive control of stochastic linear discrete-time system
 s
DTSTART:20150529T101500
DTSTAMP:20260406T184700Z
UID:288d6af5a78c6e10844e961f49d1454b2c5308dbfab61fa5a1171bdc
CATEGORIES:Conferences - Seminars
DESCRIPTION:Marcelo Farina\nThe problem of designing robust deterministic 
 model predictive control (MPC) schemes has nowadays many solutions. Howeve
 r\, the available approaches can\nbe computationally demanding\, since the
 y either require the solution to difficult on-line min-max optimization pr
 oblems\, or the off-line computation of polytopic robust positive invarian
 t sets. In addition they are conservative\, mainly because they (implicitl
 y or explicitly) rely on worst-case approaches. If the uncertainties or th
 e state and control disturbances are characterized as stochastic processes
 \, constraints should be reformulated in a probabilistic framework\, and t
 he available knowledge on the characteristics of the process noise (e.g.\,
  probability density function) should be properly accounted for in the des
 ign phase.\nThese reasons have motivated the development of MPC algorithms
  for systems affected by stochastic noise and subject to probabilistic sta
 te and/or input constraints.\nIn this seminar we discuss and classify some
  possible approaches addressing this issue in the literature. Then\, we de
 scribe a recently-proposed MPC algorithm for linear discrete-time systems 
 affected by a possibly unbounded additive noise and subject to probabilist
 ic constraints and its properties.\nApplication examples are finally discu
 ssed.\nBio: Marcello Farina received the Laurea degree in Electronic Engin
 eering in 2003 and the PhD degree in Information Engineering in 2007\, bot
 h from the Politecnico di Milano. In 2005 he was visiting student at the I
 nstitute for Systems Theory and Automatic Control\, Stuttgart\, Germany. H
 e is presently Associate Professor at Dipartimento di Elettronica\, Inform
 azione e Bioingegneria\, Politecnico di Milano. His research interests inc
 lude distributed and decentralized state estimation and control\, stochast
 ic model predictive control\, and applications\, e.g.\, mobile robots\, se
 nsor networks\, and energy supply systems.
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STATUS:CONFIRMED
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