Sliding Mode Observer-Based Fault Detection in Continuous Time Linear Switched Systems

Shafqat Ali, Muhammad Taskeen Raza, Ghulam Abbas, Nasim Ullah, Sattam Al Otaibi and Hao Luo
Additional contact information
Shafqat Ali: Department of Control Science and Engineering, Harbin Institute of Technology, Harbin 150001, China
Muhammad Taskeen Raza: Department of Electrical Engineering, Lahore College for Women University, Lahore 54000, Pakistan
Ghulam Abbas: Department of Electrical Engineering, The University of Lahore, Lahore 54000, Pakistan
Nasim Ullah: Department of Electrical Engineering, College of Engineering, Taif University, Taif 21944, Saudi Arabia
Sattam Al Otaibi: Department of Electrical Engineering, College of Engineering, Taif University, Taif 21944, Saudi Arabia
Hao Luo: Department of Control Science and Engineering, Harbin Institute of Technology, Harbin 150001, China

Energies, 2022, vol. 15, issue 3, 1-15

Abstract: This paper studies the problem of fault detection for continuous time linear switched systems in the presence of disturbance. For this purpose, a fault detection sliding mode observer approach is designed to generate the residual signal. To minimize the effect of disturbance from the residual, the problem is formulated into H∞ filtering technique to increase more robustness. To deal with the issue of the switched systems stability, the Lyapunov-Krasovskii functional method is utilized along with average dwell time, and linear matrix inequalities are formulated to derive the sufficient conditions. The residual signal is evaluated, and an adaptive threshold is computed for both modes of the switched system. Finally, a simulation example for a case study of boost converter and a numerical example with both abrupt and incipient faults are illustrated to prove the efficacy of the proposed method.

Keywords: fault detection; H? control; linear matrix inequalities; sliding mode observer; switched systems (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/15/3/1090/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/3/1090/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:15:y:2022:i:3:p:1090-:d:740438

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().