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Neural network-based adaptive reliable control for nonlinear Markov jump systems against actuator attacks

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Abstract

In this paper, the reliable control problem for a type of uncertain Markov jump systems subjected to actuator failures, malicious attacks and partially unknown transition rates (PUTRs) is under consideration. Aiming at tackling the actuator partial failures, structural uncertainty and unknown actuator attacks thoroughly, a novel adaptive neural network based sliding mode controller synthesis is developed, which confirms that the system trajectory can be moved onto the devised sliding mode surface in finite time and remain the expected performance. Then, the analysis process for stochastic stability of the desirable sliding motion with a new sufficient criterion is carried out for the closed-loop plant with uncertain PUTRs. Finally, the F-404 aircraft engine model as a simulation example of the investigated system is selected to verify the feasibility of the design method.

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Data Availability

The data that supports the findings of this study is available from the corresponding author upon reasonable request.

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Funding

This work is supported by the National Natural Science Foundation of China under grants 61803217, 62003231 and the Team Plan for Youth Innovation of Universities in Shandong Province under grant 2022KJ142.

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Authors and Affiliations

  1. School of Automation, Qingdao University; Shandong Key Laboratory of Industrial Control Technology, Qingdao University, Qingdao, 266071, China

    Junye Zhang & Zhen Liu

  2. School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, 215000, China

    Baoping Jiang

Authors
  1. Junye Zhang
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  2. Zhen Liu
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  3. Baoping Jiang
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Correspondence to Zhen Liu.

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Zhang, J., Liu, Z. & Jiang, B. Neural network-based adaptive reliable control for nonlinear Markov jump systems against actuator attacks. Nonlinear Dyn 111, 13985–13999 (2023). https://doi.org/10.1007/s11071-023-08537-0

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