DocumentCode :
2614146
Title :
Receding-horizon estimation for noisy nonlinear discrete-time systems
Author :
Alessandri, A. ; Baglietto, M. ; Battistelli, G. ; Parisini, T.
Author_Institution :
Inst. of Intelligent Syst. for Autom., ISSIA-CNR Nat. Res. Council of Italy, Genova, Italy
Volume :
6
fYear :
2003
fDate :
9-12 Dec. 2003
Firstpage :
5825
Abstract :
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.
Keywords :
discrete time systems; minimax techniques; nonlinear control systems; stability; state estimation; min-max algorithm; neural networks; nonlinear discrete-time systems; parametrized nonlinear approximators; receding-horizon estimator; receding-horizon filter; sliding-window strategy; stability; Additive noise; Cost function; Estimation error; Filters; Neural networks; Noise measurement; Nonlinear equations; Stability; State estimation; Upper bound;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Decision and Control, 2003. Proceedings. 42nd IEEE Conference on
ISSN :
0191-2216
Print_ISBN :
0-7803-7924-1
Type :
conf
DOI :
10.1109/CDC.2003.1271934
Filename :
1271934
Link To Document :
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