Please use this identifier to cite or link to this item:
|Scopus||Web of Science®||Altmetric|
|Title:||Constrained state estimation for stochastic jump systems: moving horizon approach|
|Citation:||International Journal of Systems Science, 2017; 48(5):1009-1021|
|Publisher:||Taylor & Francis|
|Qing Sun, Cheng-Chew Lim, Fei Liu|
|Abstract:||We discuss the state estimation advantages for a class of linear discrete-time stochastic jump systems, in which a Markov process governs the operation mode, and the state variables and disturbances are subject to inequality constraints. The horizon estimation approach addressed the constrained state estimation problem, and the Bayesian network technique solved the stochastic jump problem. The moving horizon state estimator designed in this paper can produce the constrained state estimates with a lower error covariance than under the unconstrained counterpart. This new estimation method is used in the design of the restricted state estimator for two practical applications.|
|Keywords:||Filter; stochastic jump systems; state estimation; moving horizon estimation; constraints|
|Rights:||© 2016 Informa UK Limited, trading as Taylor & Francis Group|
|Appears in Collections:||Aurora harvest 3|
Electrical and Electronic Engineering publications
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.