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Journal of Projectiles, Rockets, Missiles and Guidance >

2019 , Vol. 39 >Issue 1: 45 - 49

DOI: https://doi.org/10.15892/j.cnki.djzdxb.2019.01.010

Application of Improved Wavelet Neural Network to Rocket Fire Extinguishing Servo System

  • HOU Runmin ,
  • FANG Anguo ,
  • HU Da ,
  • HOU Yuanlong
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  • 1 School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
    2 Hubei Jiangshan Heavy Industries Co. Ltd, Hubei Xiangyang 441057, China

Received date: 2018-08-29

  Online published: 2025-05-21

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Abstract

This paper proposes a fuzzy wavelet neural network with improved Levenberg Marquardt algorithm (WFNN-LM) to control the nonlinearity, wide variations in loads, time variation and uncertain disturbance of the ac servo system. The particle Levenberg Marquardt has the advantages of avoiding the unstable system caused by local minimum and fast convergence rate. Therefore using Levenberg Marquardt algorithm can optimize the network weights and threshold value parameter of wavelet neural network. Finally, the result of the simulation and the prototype test verified the good performance of the proposed method on control accuracy, applicability and robustness in the conditions of parameter perturbation and load disturbance.

Key words： AC servo system; fuzzy wavelet neural network; Levenberg-Marquardt algorithm

Cite this article

HOU Runmin , FANG Anguo , HU Da , HOU Yuanlong . Application of Improved Wavelet Neural Network to Rocket Fire Extinguishing Servo System[J]. Journal of Projectiles, Rockets, Missiles and Guidance, 2019 , 39(1) : 45 -49 . DOI: 10.15892/j.cnki.djzdxb.2019.01.010

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