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Design of robust nonfragile fault detection filter for uncertain dynamic systems with quantization

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  • Xiong, Jun
  • Chang, Xiao-Heng
  • Yi, Xiaojian

Abstract

This paper investigates the fault detection problem for uncertain linear systems with respect to signal quantization. The measurement output transmitted via the digital communication link is considered to be quantized by a dynamic quantizer. Moreover, different from most of existing results on fault detection where the residual generator is assumed to be realized perfectly as the designed one, this study takes the inaccuracy and uncertainty on the implementation of residual generator into account. This paper pays much attention to designing a fault detection filter with quantization as the residual generator and formulates the design problem into the H∞ framework. The objective is to guarantee the asymptotical stability and prescribed performance of the residual system. The S-procedure and a two-step approach are adopted to handle the effects of quantization and uncertainties on residual system. Corresponding design conditions of a robust fault detection filter and a robust nonfragile ones are derived in the form of linear matrix inequalities. Finally, the efficiency of the theoretical results is illustrated by the numerical example.

Suggested Citation

  • Xiong, Jun & Chang, Xiao-Heng & Yi, Xiaojian, 2018. "Design of robust nonfragile fault detection filter for uncertain dynamic systems with quantization," Applied Mathematics and Computation, Elsevier, vol. 338(C), pages 774-788.
  • Handle: RePEc:eee:apmaco:v:338:y:2018:i:c:p:774-788
    DOI: 10.1016/j.amc.2018.06.022
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    References listed on IDEAS

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    1. Li, Jian & Wu, Chun-Yu, 2017. "Finite-time fault detection filter design for discrete-time interconnected systems with average dwell time," Applied Mathematics and Computation, Elsevier, vol. 313(C), pages 259-270.
    2. Du, Dongsheng, 2017. "Fault detection for discrete-time linear systems based on descriptor observer approach," Applied Mathematics and Computation, Elsevier, vol. 293(C), pages 575-585.
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    Cited by:

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    15. Wang, Junlan & Wang, Xin & Wang, Yantao & Zhang, Xian, 2021. "Non-reduced order method to global h-stability criteria for proportional delay high-order inertial neural networks," Applied Mathematics and Computation, Elsevier, vol. 407(C).
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    19. Ye, Dan & Li, Xiehuan, 2020. "Event-triggered fault detection for continuous-time networked polynomial-fuzzy-model-based systems," Applied Mathematics and Computation, Elsevier, vol. 366(C).
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    21. Yan, Yan & Wu, Libing & Yan, Weijun & Hu, Yuhan & Zhao, Nannan & Chen, Ming, 2022. "Finite-time event-triggered fault-tolerant control for a family of pure-feedback systems," Applied Mathematics and Computation, Elsevier, vol. 426(C).
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    24. Gu, Yang & Shen, Mouquan & Ren, Yuesheng & Liu, Hongxia, 2020. "H∞ finite-time control of unknown uncertain systems with actuator failure," Applied Mathematics and Computation, Elsevier, vol. 383(C).

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