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Mixed l1/l fault detection observer design for delayed 2D positive systems in FM LSS Models

  • Zhaoxia Duan(corresponding author)
    ,
  • Yuchen Han
    ,
  • Zhengrong Xiang
    ,
*Corresponding author for this work
  • Southeast University, Nanjing
    ,
  • Hohai University
    ,
  • Nanjing University of Science and Technology
    ,
  • COMSATS University Islamabad
Research Output:
Contribution to journal
Article
Peer-review

Abstract

This study is concerned with the problem of the mixed l1/l fault detection (FD) observer for delayed two-dimensional (2D) positive systems (PSs). The necessary and sufficient conditions (NSCs) are derived under which the residual error system is asymptotically stable (AS) and has the prescribed performance level. The conservatism is greatly reduced compared to the existing results. A new performance analysis method and the mixed l1/l FD observer design are presented. Firstly, the calculation of l1/l index for delayed 2D PSs is proposed by establishing the equivalence between the delayed system and the higher dimensional delay-free system in sense of l1 and l indexes. Secondly, NSCs are developed such that the delayed 2D PS is AS with a desired mixed l1/l performance. Thirdly, the sufficient conditions of the observer design are further developed based on linear programming. An iterative algorithm is formulated to minimize and maximize the impact of system disturbances and faults on the output signal, respectively. Finally, we present two examples to verify the superiority of the obtained results.

Publication Information

Output type

Research Output:
Contribution to journal
Article
Peer-review

Original language

English

Pages from-to (Number of pages)

Pages 361-373 (13 pages)

Journal (Volume, Issue Number)

ISA Transactions (Volume 136)

Publication milestones

  • Accepted/In press - 26/11/2022
  • E-pub ahead of print - 30/11/2022
  • Published - 30/11/2022

Publication status

Published - 30/11/2022

ISSN

0019-0578

Publication IDs

  • Scopus: 85144963377
  • PubMed: 36503617