Distributed Fault Detection for Linear Time-Varying Multi-Agent Systems With Relative Output Information

• Published in: IEEE access Vol. 9; pp. 42933 - 42946
• Main Authors: Zou, Peilu, Wang, Ping, Yu, Chengpu
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Discrete time systems; distributed algorithms; Estimation; Fault detection; fault diagnosis; filtering theory; heterogeneous networks; Kalman filters; linear systems; minimization; Multi-agent systems; Multiagent systems; Observers; Quadratic forms; Robot sensing systems; Stacking; Time-varying systems
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2021.3066109

This paper investigates the distributed fault detection problem for linear discrete time-varying heterogeneous multi-agent systems under relative output information. Due to the lack of absolute outputs, an augmented model is built by stacking all local relative output information. Then, the fault detection problem consisting of residual-generation and residual-evaluation is handled using the <inline-formula> <tex-math notation="LaTeX">H_{\infty } </tex-math></inline-formula> filtering framework. The residual-generation problem is actually a minimization problem of an indefinite quadratic form, and the Krein space-Kalman filtering theory is applied, which results in a low computational burden despite the time-varying characteristic. Using the Krein space theory, a necessary and sufficient condition for the minimum is derived, and a residual-generation algorithm is developed. Further, a residual-evaluation mechanism is designed by constructing an evaluation function and detecting faults by comparing it with a threshold. Finally, two illustrative examples are given to demonstrate the effectiveness of the proposed fault detection approach.