Design of Asymptotic Estimators: An Approach Based on Neural Networks and Nonlinear Programming (Articolo in rivista)

Type
Label
  • Design of Asymptotic Estimators: An Approach Based on Neural Networks and Nonlinear Programming (Articolo in rivista) (literal)
Anno
  • 2007-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1109/TNN.2006.883015 (literal)
Alternative label
  • Angelo Alessandri; Cristiano Cervellera; Marcello Sanguineti (2007)
    Design of Asymptotic Estimators: An Approach Based on Neural Networks and Nonlinear Programming
    in IEEE transactions on neural networks
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Angelo Alessandri; Cristiano Cervellera; Marcello Sanguineti (literal)
Pagina inizio
  • 86 (literal)
Pagina fine
  • 96 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 18 (literal)
Rivista
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo
  • 1 (literal)
Note
  • Scopu (literal)
  • ISI Web of Science (WOS) (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • Angelo Alessandri: Department of Production Engineering, Thermoenergetics, and Mathematical Models (DIPTEM), University of Genoa, Genova 16129, Italy Cristiano Cervellera: Institute of Intelligent Systems for Automation, National Research Council of Italy (ISSIA-CNR), Genova 16149, Italy Marcello Sanguineti: Department of Communications, Computer and System Sciences (DIST), University of Genoa, Genova 16145, Italy (literal)
Titolo
  • Design of Asymptotic Estimators: An Approach Based on Neural Networks and Nonlinear Programming (literal)
Abstract
  • A methodology to design state estimators for a class of nonlinear continuous-time dynamic systems that is based on neural networks and nonlinear programming is proposed. The estimator has the structure of a Luenberger observer with a linear gain and a parameterized (in general, nonlinear) function, whose argument is an innovation term representing the difference between the current measurement and its prediction. The problem of the estimator design consists in finding the values of the gain and of the parameters that guarantee the asymptotic stability of the estimation error. Toward this end, if a neural network is used to take on this function, the parameters (i.e., the neural weights) are chosen, together with the gain, by constraining the derivative of a quadratic Lyapunov function for the estimation error to be negative definite on a given compact set. It is proved that it is sufficient to impose the negative definiteness of such a derivative only on a suitably dense grid of sampling points. The gain is determined by solving a Lyapunov equation. The neural weights are searched for via nonlinear programming by minimizing a cost penalizing grid-point constraints that are not satisfied. Techniques based on low-discrepancy sequences are applied to deal with a small number of sampling points, and, hence, to reduce the computational burden required to optimize the parameters. Numerical results are reported and comparisons with those obtained by the extended Kalman filter are made. (literal)
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