A neural state estimator with bounded errors for nonlinear systems

Author

Alessandri, Angelo ; Baglietto, Marco ; Parisini, Thomas ; Zoppoli, Riccardo

Author_Institution

Naval Autom. Inst., Nat. Res. Council of Italy, Genova, Italy

Volume

44

Issue

11

fYear

1999

fDate

11/1/1999 12:00:00 AM

Firstpage

2028

Lastpage

2042

Abstract

A neural state estimator is described, acting on discrete-time nonlinear systems with noisy measurement channels. A sliding-window quadratic estimation cost function is considered and the measurement noise is assumed to be additive. No probabilistic assumptions are made on the measurement noise nor on the initial state. Novel theoretical convergence results are developed for the error bounds of both the optimal and the neural approximate estimators. To ensure the convergence properties of the neural estimator, a minimax tuning technique is used. The approximate estimator can be designed offline in such a way as to enable it to process on line any possible measure pattern almost instantly

Keywords

convergence; discrete time systems; errors; minimax techniques; neurocontrollers; nonlinear systems; state estimation; bounded errors; convergence; discrete-time nonlinear systems; error bounds; measurement noise; minimax tuning technique; neural state estimator; noisy measurement channels; sliding-window quadratic estimation cost function; Additive noise; Control systems; Convergence; Cost function; Minimax techniques; Noise measurement; Nonlinear systems; Observers; State estimation; Stochastic processes;

fLanguage

English

Journal_Title

Automatic Control, IEEE Transactions on

Publisher

ieee

ISSN

0018-9286

Type

jour

DOI

10.1109/9.802911

Filename

802911