Premium
Communication delays and data losses in distributed adaptive high‐gain EKF
Author(s) -
Rashedi Mohammad,
Liu Jinfeng,
Huang Biao
Publication year - 2016
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.15351
Subject(s) - estimator , extended kalman filter , kalman filter , nonlinear system , convergence (economics) , control theory (sociology) , computer science , process (computing) , state (computer science) , compensation (psychology) , control engineering , engineering , algorithm , mathematics , artificial intelligence , psychology , statistics , physics , control (management) , quantum mechanics , psychoanalysis , economics , operating system , economic growth
In this work, we consider distributed adaptive high‐gain extended Kalman filtering for nonlinear systems subject to data losses and delays in communications. Specifically, we consider a class of nonlinear systems that consist of several subsystems interacting with each other via their states. A local adaptive high‐gain extended Kalman filter is designed for each subsystem and the distributed estimators communicate to exchange the information. Each subsystem estimator takes the advantage of a predictor accounting for the delays and data losses simultaneously. The predictor of each subsystem is used to generate state predictions of interacting subsystems for interaction compensation. To get a reliable prediction, the predictors are designed based on a prediction‐update algorithm. The convergence of the proposed distributed state estimation is ensured under sufficient conditions handling communication delays and data losses. Finally, a chemical process example is used to evaluate the applicability and effectiveness of the proposed design. © 2016 American Institute of Chemical Engineers AIChE J , 62: 4321–4333, 2016