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access icon free Fault estimation filter design for discrete-time Takagi–Sugeno fuzzy systems

  • Author(s): Da-Wei Ding 1 ; Xin Du 2 ; Xiangpeng Xie 3 ; Mo Li 1
    • View affiliations
    • Affiliations: 1: School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, People's Republic of China ;
      2: School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200072, People's Republic of China ;
      3: Institute of Advanced Technology, Nanjing University of Posts and Telecommunications, Nanjing 210023, People's Republic of China
  • Source: Volume 10, Issue 18, 12 December 2016, p. 2456 – 2465
    DOI:  10.1049/iet-cta.2016.0318 , Print ISSN 1751-8644, Online ISSN 1751-8652
© The Institution of Engineering and Technology
Received 28/03/2016, Accepted 31/07/2016, Revised 06/07/2016, Published 18/08/2016

This study is concerned with the fault estimation problem of discrete-time Takagi–Sugeno fuzzy systems with low-frequency faults. A fault estimation filter is designed to solve this problem. To make the estimation error as small as possible in the presence of high-frequency disturbances, the filter is designed to satisfy two finite-frequency H ∞ performance indices simultaneously. An algorithm is proposed to calculate the parameters of the desired filter. The obtained filter can estimate the faults in low-frequency domain effectively, e.g. stuck faults. For comparison, a full-frequency fault estimation method is also given. An example is given to illustrate advantages of the proposed finite-frequency method.

Inspec keywords: H∞ filters; fuzzy set theory; discrete time systems

Other keywords: high-frequency disturbances; fault estimation problem; full-frequency fault estimation method; fault estimation filter design; finite-frequency H∞ performance indices; fault estimation filter; low-frequency faults; finite-frequency method; discrete-time Takagi–Sugeno fuzzy systems

Subjects: Combinatorial mathematics; Discrete control systems; Signal processing theory

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