Ding, Steven X.; Yin, Shen; Zhang, Ping; Ding, Eve L.; Naik, Amol S.:
An Approach to Data-Driven Adaptive Residual Generator Design and Implementation
In: 7th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes - Barcelona, 2009, S. 941 - 946
2009Buchaufsatz/Kapitel in Sammelwerk
TechnikFakultät für Ingenieurwissenschaften » Elektrotechnik und Informationstechnik » Automatisierungstechnik und komplexe Systeme
Titel:
An Approach to Data-Driven Adaptive Residual Generator Design and Implementation
Autor*in:
Ding, Steven X.UDE
GND
134302427
LSF ID
2347
ORCID
0000-0002-5149-5918ORCID iD
Sonstiges
der Hochschule zugeordnete*r Autor*in
;Yin, Shen;Zhang, Ping;Ding, Eve L.;Naik, Amol S.
DOI
10.3182/20090630-4-ES-2003.00155
Erschienen in
7th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes
Erscheinungsort:
Barcelona
Erscheinungsjahr:
2009
in:
S. 941 - 946

Abstract:

This paper addresses data-driven design and implementation of adaptive observer based residual generators for discrete-time systems. The basic idea behind this study is the application of an one-to-one mapping between a parity vector and the solution of Luenberger equations and the data-driven identification of parity space. For the realization of the adaptive residual generation, standard adaptive technique is applied. The proposed approach is demonstrated on the laboratory three-tank-system.