By Topic

Receding-horizon estimation for noisy nonlinear discrete-time systems

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Alessandri, A. ; Inst. of Intelligent Syst. for Autom., ISSIA-CNR Nat. Res. Council of Italy, Genova, Italy ; Baglietto, M. ; Battistelli, G. ; Parisini, T.

The problem of constructing a receding-horizon estimator for nonlinear discrete-time systems affected by disturbances has been addressed. The noises are assumed to be bounded, additive, and acting on both state and measurement equations. The estimator is designed according to a sliding-window strategy, i.e., so that it minimizes a receding-horizon estimation cost function. The stability of the resulting filter has been investigated and an upper bound on the estimation error has been found. Such a filter can be suitably approximated by parametrized nonlinear approximators as, for example, neural networks. A min-max algorithm turns out to be well-suited to selecting these parameters, as it allows one to guarantee the stability of the error dynamics of the approximate receding-horizon filter. This estimator is designed off line in such a way as to be able to process any possible information pattern. This enables it to generate state estimates almost instantly with a small on-line computational burden.

Published in:

Decision and Control, 2003. Proceedings. 42nd IEEE Conference on  (Volume:6 )

Date of Conference:

9-12 Dec. 2003