In this paper, the design of an output predictor in a system with random scarce sampling is addressed. A model based predictor that takes into account the past measured outputs is used, and a Lyapunov function of the estimation error is used for design purposes. The Lyapunov design problem becomes a feasibility problem over a set of linear matrix inequalities applying the Schur complement formula. Three different design approaches have been developed. Some examples show the performances of each approach.
%0 Conference Paper
%1 Sala2005b
%A Pe\ narrocha, Ignacio
%A Sala, Antonio
%A Sanchis, Roberto
%A Albertos, Pedro
%B 16th IFAC World Congress
%C Prague, Czech Republic
%D 2005
%K convergence,missing-data,random linear matrix,martingale periods,unconventional sampling sampling,time-varying
%N c
%P 303--308
%R 10.3182/20050703-6-CZ-1902.00051
%T Output prediction under random measurements. An LMI approach
%U http://www.nt.ntnu.no/users/skoge/prost/proceedings/ifac2005/Fullpapers/03611.pdf
%X In this paper, the design of an output predictor in a system with random scarce sampling is addressed. A model based predictor that takes into account the past measured outputs is used, and a Lyapunov function of the estimation error is used for design purposes. The Lyapunov design problem becomes a feasibility problem over a set of linear matrix inequalities applying the Schur complement formula. Three different design approaches have been developed. Some examples show the performances of each approach.
%@ 0-08-045108-X
@inproceedings{Sala2005b,
abstract = {In this paper, the design of an output predictor in a system with random scarce sampling is addressed. A model based predictor that takes into account the past measured outputs is used, and a Lyapunov function of the estimation error is used for design purposes. The Lyapunov design problem becomes a feasibility problem over a set of linear matrix inequalities applying the Schur complement formula. Three different design approaches have been developed. Some examples show the performances of each approach.},
added-at = {2013-03-23T10:56:50.000+0100},
address = {Prague, Czech Republic},
author = {Pe\ {n}arrocha, Ignacio and Sala, Antonio and Sanchis, Roberto and Albertos, Pedro},
biburl = {https://www.bibsonomy.org/bibtex/2890f1981dcfb142bd430f2050bb8e773/ipenarro},
booktitle = {16th IFAC World Congress},
doi = {10.3182/20050703-6-CZ-1902.00051},
file = {:X$\backslash$:/PDF files/biblio4/ifac-safeprocess/ifac-wc-2005016-01jul-0050pena.pdf:pdf},
interhash = {7d09a1b48be4c375082d6a338cfadc51},
intrahash = {890f1981dcfb142bd430f2050bb8e773},
isbn = {0-08-045108-X},
keywords = {convergence,missing-data,random linear matrix,martingale periods,unconventional sampling sampling,time-varying},
number = {c},
pages = {303--308},
timestamp = {2013-03-23T10:56:50.000+0100},
title = {{Output prediction under random measurements. An LMI approach}},
url = {http://www.nt.ntnu.no/users/skoge/prost/proceedings/ifac2005/Fullpapers/03611.pdf},
year = 2005
}