Action functional stochastic H∞ estimation for nonlinear discrete time systems
AuthorCharalambous, Charalambos D.
Djouadi, S. M.
SourceProceedings of the IEEE Conference on Decision and Control
Proceedings of the IEEE Conference on Decision and Control
Google Scholar check
MetadataShow full item record
This paper presents an action functional, sample path optimization technique, for formulating and solving nonlinear discrete-time stochastic H∞ estimation problems. These H∞ problems are formulated as minimax dynamic games in which the maximizing players are stochastic square summable disturbances, while the minimizing players are the state estimates. Certain action functionals are defined which play the role of information state and its adjoint in converting the minimax game into a fully observable game. Subsequently, a verification theorem is derived.
Showing items related by title, author, creator and subject.
Distributed network size estimation and average degree estimation and control in networks isomorphic to directed graphs Shames, I.; Charalambous, T.; Hadjicostis, Christoforos N.; Johansson, M. (2012)Many properties of interest in graph structures are based on the nodes' average degree (i.e., the average number of edges incident to/from each node). In this work, we present asynchronous distributed algorithms, based on ...
Least-Square estimation for nonlinear systems with applications to phase and envelope estimation in wireless fading channels Socratous, Y.; Charalambous, Charalambos D.; Georghiades, C. N. (2008)This paper addresses the problem of nonlinear Least-Square estimation. A new approach is presented which employees a change of probability measure technique to derive recursive equations for conditional means of nonlinear ...
Maximum Likelihood parameter estimation from incomplete data via the sensitivity equations: The continuous-time case Charalambous, Charalambos D. (IEEE, 1999)The problem of estimating the parameters for continuous-time partially observed systems is discussed. New exact filters for obtaining Maximum Likelihood (ML) parameter estimates via the Expectation Maximization algorithm ...