Active state estimation for nonlinear systems: a neural approximation approach.
In this paper, we consider the problem of actively providing an estimate of the state of a stochastic dynamic system over a (possibly long) finite time horizon. The active estimation problem (AEP) is formulated as a stochastic optimal control one, in which the minimization of a suitable uncertainty measure is carried out. Toward this end, the use of the Renyi entropy as an information measure is proposed and motivated. A neural control scheme, based on the application of the extended Ritz method (ERIM) and on the use of a Gaussian sum filter (GSF), is then presented. Simulation results show the effectiveness of the proposed approach.
Department of Electrical Engineering and Computer Science, University of Liège, B-4000 Liège, Belgium. l.scardovi@ulg.ac.be
Luca
L
Scardovi
Marco
M
Baglietto
Thomas
T
Parisini
D000465:Algorithms; D003198:Computer Simulation; D003661:Decision Support Techniques; D005246:Feedback; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics