DocumentCode :
2907534
Title :
Kernel-based non-asymptotic state estimation for linear continuous-time systems
Author :
Pin, Gilberto ; Lovera, Marco ; Assalone, Andrea ; Parisini, Thomas
Author_Institution :
Electrolux Prof. S.p.A., Italy
fYear :
2013
fDate :
17-19 June 2013
Firstpage :
3123
Lastpage :
3128
Abstract :
This work deals with a novel theoretical framework, based on the algebra of Volterra linear integral operators, aimed at designing non-asymptotic state observers for continuous-time SISO linear systems. We show that the design of observers with finite-time convergence of the estimation error can be carried out by appropriately choosing the kernels of Volterra operators applied to the measured input and output signals. The kernel-based state estimator can be implemented as a finite-dimensional linear time-varying dynamical system, that is BIBO stable with respect to the input and output injections. The properties of the kernels guaranteeing non-asymptotic convergence of the state estimate are analyzed and simulations are given to compare the proposed methodology with existing approaches.
Keywords :
Volterra equations; continuous time systems; control system synthesis; linear systems; multidimensional systems; observers; stability; time-varying systems; BIBO stability; Volterra linear integral operators; continuous-time SISO linear systems; finite-dimensional linear time-varying dynamical system; finite-time convergence; kernel-based nonasymptotic state estimation; kernel-based state estimator; linear continuous-time systems; nonasymptotic state observers; Convergence; Equations; Kernel; Noise; Noise measurement; Observers; Vectors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
American Control Conference (ACC), 2013
Conference_Location :
Washington, DC
ISSN :
0743-1619
Print_ISBN :
978-1-4799-0177-7
Type :
conf
DOI :
10.1109/ACC.2013.6580311
Filename :
6580311
Link To Document :
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