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SUMMARY:Enhanced model-based experiment design techniques for parameter id
 entification in complex dynamic systems under uncertainty - Bezzo\, F (Uni
 versit degli Studi di Padova )
DTSTART:20110719T090000Z
DTEND:20110719T100000Z
UID:TALK32091@talks.cam.ac.uk
CONTACT:Mustapha Amrani
DESCRIPTION:A wide class of physical systems can be described by dynamic d
 eterministic models expressed in the form of systems of differential and a
 lgebraic equations. Once a dynamic model structure is found adequate to re
 present a physical system\, a set of identification experiments needs to b
 e carried out to estimate the set of parameters of the model in the most p
 recise and accurate way. Model-based design of experiments (MBDoE) techniq
 ues represent a valuable tool for the rapid assessment and development of 
 dynamic deterministic models\, allowing for the maximisation of the inform
 ation content of the experiments in order to support and improve the param
 eter identification task. However\, uncertainty in the model parameters or
  in the model structure itself or in the representation of the experimenta
 l facility may lead to design procedures that turn out to be scarcely info
 rmative. Additionally\, constraints may occur to be violated\, thus making
  the experiment unfeasible or even unsafe. Handling uncertainty is a compl
 ex and still open problem\, although over the last years significant resea
 rch effort has been devoted to tackle some issues in this area. Here\, som
 e approaches developed at CAPE-Lab at University of Padova will be critica
 lly discussed. First Online Model-Based Redesign of Experiment (OMBRE) str
 ategies will be taken into account. In OMBRE the objective is to exploit t
 he information as soon as soon as it is generated by the running experimen
 t. The manipulated input profiles of the running experiment are updated by
  performing one or more intermediate experiment designs (i.e.\, redesigns)
 \, and each redesign is performed adopting the current value of the parame
 ter set. In addition\, a model updating policy including disturbance estim
 ation embedded within an OMBRE strategy (DE-OMBRE) can be considered. In t
 he DE-OMBRE approach\, an augmented model lumping the effect of systematic
  errors is considered to estimate both the states and the system outputs i
 n a given time frame\, updating the constraint conditions in a consistent 
 way as soon as the effect of unknown disturbances propagates in the system
 . Backoff-based MBDoE\, where uncertainty is explicitly accounted for so a
 s to plan a test that is both optimally informative and safe by design\, i
 s eventually discussed.\n
LOCATION:Seminar Room 1\, Newton Institute
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