Finite-time memory fault detection filter design for nonlinear discrete systems with deception attacks

Weilu Chen, Jun Hu, Zhihui Wu, Xiaoyang Yu and Dongyan Chen

International Journal of Systems Science, 2020, vol. 51, issue 8, 1464-1481

Abstract: In this paper, the finite-time memory fault detection filter (MFDF) is designed for nonlinear discrete systems with randomly occurring deception attacks, where the phenomenon of the randomly occurring deception attacks is characterised by a Bernoulli distributed random variable with known probability. To be specific, the finite-horizon data are employed to construct the MFDF. The main purpose of this paper is to design the MFDF such that, for all nonlinearities, external disturbances and randomly occurring deception attacks, the resultant augmented system is finite-time stable and attains the $H_{\infty } $H∞ performance. Accordingly, some sufficient conditions are developed to guarantee the existence of the desired fault detection filter parameters, where the solvability of the addressed problem is verified by the feasibility of certain matrix inequalities. Moreover, in order to show that the MFDF has better detection performance, a non-memory fault detection filter (NMFDF) is constructed to compare with the MFDF. Finally, two numerical simulations are utilised to illustrate the effectiveness of the proposed fault detection strategies and explain the superiority of the proposed MFDF.

Date: 2020
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DOI: 10.1080/00207721.2020.1765219

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