Open AccessEvent‐triggered state estimator for stochastic systems with unknown inputs
Author(s) -
Li Wenling,
Jia Yingmin,
Du Junping
Publication year - 2017
Publication title -
iet signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.384
H-Index - 42
eISSN - 1751-9683
pISSN - 1751-9675
DOI - 10.1049/iet-spr.2016.0293
Subject(s) - estimator , kalman filter , control theory (sociology) , computer science , minimum mean square error , state (computer science) , bounded function , generator (circuit theory) , event (particle physics) , term (time) , mean squared error , mathematics , algorithm , statistics , control (management) , artificial intelligence , mathematical analysis , power (physics) , physics , quantum mechanics
This article studies the problem of state estimation for stochastic systems with unknown inputs. To reduce the communication cost from the sensor to the remote processor, an event‐triggered communication mechanism is proposed in terms of an event generator function for the innovation vectors. The event‐triggered estimator is developed by introducing an input term in the steady‐state Kalman filter for the corresponding nominal system. The input gain matrix is determined by treating the nominal estimator error dynamics as the desired performance. It is shown that the estimation error is bounded in mean square under certain conditions. A numerical example is provided to verify the effectiveness of the proposed estimator.