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SUMMARY:Fundamentals of offset-free MPC algorithms and recent advances
DTSTART:20161028T101500
DTEND:20161028T111500
DTSTAMP:20260916T050048Z
UID:73d3e8f97ed63c6aedc9c7c5ea508521b9abdd164afefff227d27922
CATEGORIES:Conferences - Seminars
DESCRIPTION:Prof. Gabriele Pancocchia\, University of Pisa\nBio : Gabriele
  Pannocchia received the M.S. and Ph.D. degrees in Chemical Engineering fr
 om the University of Pisa (Italy) in 1998 and 2002\, respectively. He held
  a Visiting Associate position at the University of Wisconsin - Madison (W
 I\, USA) in 2000/2001 and in 2008\, and a Post-Doctoral position at the Un
 iversity of Pisa from 2002 to 2006. From 2006 to 2015 he was tenured Assis
 tant Professor at the University of Pisa\, where he became Associate Profe
 ssor in 2015. Prof. Pannocchia is author of more than 90 papers in interna
 tional journals\, book chapters and in proceedings of international confer
 ences. Since 2008 he is Associate Editor of the Journal of Process Control
  and from 2013 he is Subject Editor for the same journal. He is member of 
 the Editorial Board of the journal "Processes''. He was IPC co-chair of th
 e IFAC Symposium DYCOPS 2013 held in Mumbai (India)\, and he has been keyn
 ote speaker in several international congresses (IFAC NMPC and IFAC DYCOPS
 ). His research interests include: model predictive control systems\, proc
 ess simulation and optimization\, numerical optimization\, multivariable s
 ystems identification and performance monitoring\, biomedical systems mode
 ling and applications of automatic control algorithms\, optimal planning a
 nd control for robotic systems.\nFeedback is necessary to reduce the effec
 t of disturbances and to cope with unavoidable modeling errors. Nonetheles
 s\, the way in which feedback is used to achieve offset-free tracking in t
 he presence of persistent errors or disturbances appears to be often a que
 stion of personal preference among possibledifferent methods. The general 
 goal of this talk is to describe in a tutorial way this aspect of MPC theo
 ry and design\, which is often overlooked in academic papers but is fundam
 ental for effective implementation. The talk will comprise three parts.  
 \n\n\n	First\, we present the basic results for offset-free linear MPC tra
 cking\, explaining in detail how the integral action is achieved in spite 
 of modeling errors\, and then we show that several known alternative offse
 t-free MPC algorithms (using velocity models) are special cases of the gen
 eral disturbance models. A connection with the Youla-Kucera parameterizati
 on is also established as a special case.\n	Then\, we present a comprehens
 ive description of the available results on offset-free nonlinear MPC\, an
 d we show new results on the asymptotic convergence of the estimator. Guid
 elines on disturbance modeling and observer design for nonlinear MPC are p
 resented.\n	Finally\, we extend the concepts of offset-free estimation for
  nonlinear MPC to the design an economic MPC algorithm that is able to cop
 e with persistent errors while still achieving the optimal ultimate econom
 ic performance.\n\n\nIn each part\, extensive application results are pres
 ented to show the benefits of offset-free MPC algorithms over standard one
 s\, and to clarify misconceptions and design errors that can prevent const
 raint satisfaction\, closed-loop stability\, and offset-free performance.
  
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STATUS:CONFIRMED
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