4.8 Article

Finite-Frequency H-/H∞ Memory Fault Detection Filtering Design for Uncertain Takagi-Sugeno Fuzzy Affine Systems

Journal

IEEE TRANSACTIONS ON FUZZY SYSTEMS
Volume 30, Issue 7, Pages 2595-2609

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TFUZZ.2021.3089736

Keywords

Fault detection; finite-frequency domain; H-/H-infinity filtering; memory filtering; Takagi-Sugeno (T-S) fuzzy affine systems

Funding

  1. National Natural Science Foundation of China [61773327]
  2. Research Grants Council of the Hong Kong Special Administrative Region of China [CityU/11211818]

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This article deals with the finite-frequency H-/H-infinity memory fault detection filtering problem for discrete-time Takagi-Sugeno fuzzy affine systems with norm-bounded uncertainties. It focuses on designing a piecewise affine memory filter using system historical information to ensure the resulting closed-loop filtering error system is asymptotically stable with the prescribed finite-frequency H-/H-infinity performance.
This article is concerned with the finite-frequency H-/H-infinity memory fault detection filtering problem for discrete-time Takagi-Sugeno fuzzy affine systems with norm-bounded uncertainties. The objective is to design a piecewise affine memory filter by using system historical information such that the resulting closed-loop filtering error system is asymptotically stable with the prescribed finite-frequency H-/H-infinity performance. Based on the generalized Kalman-Yakubovie-Popov lemma combined with the celebrated S-procedure, new sufficient conditions for the fuzzy affine filtering error system to have the finite-frequency H-/H(infinity )performance are given at first. By further using piecewise fuzzy quadratic Lyapunov functions and Projection lemma, the filtering analysis results for the filtering error system to be asymptotically stable with the prescribed finite-frequency performance are obtained. Then, the filtering synthesis is carried out with the aid of matrix inequality convexification techniques, and the synthesis results are described in terms of linear matrix inequalities. It is further shown that a better filtering performance can he achieved by using more system historical information. Finally, simulation is provided to verify the effectiveness of the proposed approach.

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