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SUMMARY:Optimal experiment design for open and closed loop identification 
 - Professor Michel Gevers (Université catholique de Louvain)
DTSTART:20100504T130000Z
DTEND:20100504T140000Z
UID:TALK23670@talks.cam.ac.uk
CONTACT:Dr Ioannis Lestas
DESCRIPTION:Optimal experiment design for system identification was a very
  active research topic in the 1970’s : the results at that time focused 
 on the minimization of different measures of the parameter covariance matr
 ix.  The research on this topic disappeared from the horizon for more than
  a decade. In the mid eighties new results became available that focused o
 n quality criteria that took account of the objective for which the model 
 was estimated. These results were based on approximate variance formulae f
 or the estimated transfer functions\, under the assumption that the model 
 order goes to infinity. Experiment design experienced a sudden revival of 
 activity from around 2003 under a triple  influence : the advent of new ex
 pressions for the variance of estimated quantities that did not require an
  assumption of model order  going to infinity\,  the introduction of the c
 oncept of « least costly identification design»\,  and the development  
 of new optimal design techniques for identification that  convert the opti
 mization problem into  semi-definite programs that can be solved using Lin
 ear Matrix Inequalities. In this talk we shall first  review the developme
 nt of optimal experiment design. We shall then present new results that al
 low one to solve the optimal closed loop experiment design problem\, where
  the optimization is performed jointly with respect to the controller and 
 the spectrum of the external excitation. Our results are based on the part
 ial positive definite matrix completion theorem.
LOCATION:Cambridge University Engineering Department\, LR5
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