Zhong, Maiying; Ding, Steven X.; Han, Qing-Long; He, Xiao; Zhou, Donghua:
A Krein space-based approach to event-triggered H∞ filtering for linear discrete time-varying systems
In: Automatica : A journal of IFAC, Band 135 (2022), Artikel 110001
2022Artikel/Aufsatz in ZeitschriftOA Bronze
ElektrotechnikFakultät für Ingenieurwissenschaften » Elektrotechnik und Informationstechnik » Automatisierungstechnik und komplexe Systeme
Damit verbunden: 1 Publikation(en)
Titel in Englisch:
A Krein space-based approach to event-triggered H∞ filtering for linear discrete time-varying systems
Autor*in:
Zhong, Maiying
;
Ding, Steven X.UDE
GND
134302427
LSF ID
2347
ORCID
0000-0002-5149-5918ORCID iD
Sonstiges
der Hochschule zugeordnete*r Autor*in
;
Han, Qing-Long
;
He, Xiao
;
Zhou, DonghuaUDE
LSF ID
1501
ORCID
0000-0003-0169-8490ORCID iD
Sonstiges
der Hochschule zugeordnete*r Autor*in
Erscheinungsjahr:
2022
Open Access?:
OA Bronze
Web of Science ID
Scopus ID
Sprache des Textes:
Englisch
Schlagwort, Thema:
Event-triggering mechanism ; H∞ filtering ; Krein space ; Linear discrete time-varying system ; Projection

Abstract in Englisch:

This paper is concerned with the problem of event-triggered H∞ filtering for linear discrete time-varying (LDTV) systems. Using the lifting technique, we firstly establish an equivalent relationship with a certain equivalent minimum problem of indefinite quadratic form subject to LDTV systems with non-uniform sampling periods. Then, based on Krein space projection and innovation analysis, sufficient and necessary conditions for the existence of desired filter are derived and a feasible solution is obtained in terms of Riccati recursions. Thus, an algorithm based on the time-update and event-update recursions is given for the implementation of event-triggered H∞ filtering. Different from some existing results, a new event-triggered H∞ filtering scheme is provided so that the estimation error can be completely decoupled from the event-triggered transmission error. Moreover, the new proposed Krein space approach is less conservative and more computational attractive than the existing methods based on recursive linear inequality matrix. Finally, a numerical example is given to illustrate the effectiveness of the proposed approach.