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Neural network-based event-triggered fault detection for nonlinear Markov jump system with frequency specifications

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Abstract

In this paper, a neural network-based event-triggered fault detection scheme is addressed within the finite-frequency domain for a class of nonlinear Markov jump system. Initially, an approximation model based on multilayer neural network to alternate the nonlinear Markov jump system is constructed. For the purpose of saving the communication network bandwidth, a transmission mechanism based on the event-triggered strategy is subsequently applied in which each signal is transmitted depending on the designed condition rather than the sampling period. Further, two theorems with considering the signal frequency and the applied event-triggered mechanism are derived which guarantee the fault sensitivity as well as disturbance attenuation for the augment systems in certain frequency ranges. Then, the desired filters can be synthesized by the linear solvable conditions that are derived with the aid of the previous theorems and some novel decoupling techniques. Eventually, the proposed algorithm’s efficiency is shown by a presented computational example.

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Acknowledgements

This work is supported by National Natural Science Foundation of China (Grant Nos. 61773187, 51939001, 61976033) and Natural Science Foundation of Liaoning Province (Grant No. 2019-MS-150).

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Authors and Affiliations

  1. School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China

    Qi-Dong Liu, Yue Long & Tieshan Li

  2. School of Physics, Liaoning University, Shenyang, 110036, China

    Qi-Dong Liu

  3. Department of Electrical Engineering, Yeungnam University, Kyongsan, 38541, Republic of Korea

    Ju H. Park

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  1. Qi-Dong Liu
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Correspondence to Yue Long.

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Liu, QD., Long, Y., Park, J.H. et al. Neural network-based event-triggered fault detection for nonlinear Markov jump system with frequency specifications. Nonlinear Dyn 103, 2671–2687 (2021). https://doi.org/10.1007/s11071-021-06263-z

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  • DOI: https://doi.org/10.1007/s11071-021-06263-z

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