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Multi-packet transmission aero-engine DCS neural network sliding mode control based on multi-kernel LS-SVM packet dropout online compensation

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  • Li Guangfu
  • Wang Xu
  • Ren Jia

Abstract

In view of the strong nonlinear characteristics of the multi-packet transmission Aero-engine DCS with induced delay and random packet dropout, a neural network PID approach law sliding-mode controller using sliding window strategy and multi-kernel LS-SVM packet dropout online compensation is proposed. Firstly, the time-delay term in the system model is transformed equivalently, to establish the discrete system model of multi-packet transmission without time-delay; furthermore, the construction of multi-kernel function is transformed into kernel function coefficient optimization, and the optimization problem can be solved by the chaos adaptive artificial fish swarm algorithm, then the online predictive compensation will be made for data packet dropout of multi-packet transmission through the sliding window multi-kernel LS-SVM. After that, a sliding-mode controller design method of proportional integral differential approach law based on neural network is proposed. And online adjustment of PID approach law parameters can be achieved by nonlinear mapping of neural network. Finally, Truetime is used to simulate the method. The results shows that when the packet dropout rate is 30% and 60%, the average error of packet dropout prediction of multi-kernel LS-SVM reduces 29.21% and 44.66% compared with that of combined kernel LS-SVM, and the chattering amplitude of the proposed neural network PID approach law sliding-mode controller is decreased compared with other five approach law methods respectively. This controller can ensure a fast response speed, which shows that this method can achieve a better tracking control of the aeroengine network control system.

Suggested Citation

  • Li Guangfu & Wang Xu & Ren Jia, 2020. "Multi-packet transmission aero-engine DCS neural network sliding mode control based on multi-kernel LS-SVM packet dropout online compensation," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-22, June.
  • Handle: RePEc:plo:pone00:0234356
    DOI: 10.1371/journal.pone.0234356
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    References listed on IDEAS

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    1. Hannah Jessie Rani R. & Aruldoss Albert Victoire T., 2018. "Training radial basis function networks for wind speed prediction using PSO enhanced differential search optimizer," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-35, May.
    2. Quanchao Chen & Di Wen & Xuqiang Li & Dingjun Chen & Hongxia Lv & Jie Zhang & Peng Gao, 2019. "Empirical mode decomposition based long short-term memory neural network forecasting model for the short-term metro passenger flow," PLOS ONE, Public Library of Science, vol. 14(9), pages 1-18, September.
    3. Yifei Yang & Minjia Tan & Yuewei Dai, 2017. "An improved CS-LSSVM algorithm-based fault pattern recognition of ship power equipments," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-10, February.
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