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 Gun control system based on self-organizing neuralnetwork with complementary sliding modes(PDF)

[ISSN:1007-449X/CN:23-1 408/TM]

Issue:
2018年06
Page:
114-122
Research Field:
Publishing date:

Info

Title:
 Gun control system based on self-organizing neuralnetwork with complementary sliding modes
Author(s):
  WANG Chao1 ZHOU Yong-jun2 YAN Shou-cheng2 ZHOU Wen-jun1ZHANG De-lei1 TANG Xiong1
 (1. The 28th Research Institute of China Electronics Technology Group Corporation,Nanjing 210014,China;2. The People’s Liberation Army of 63983,Wuxi 214035,China)
Keywords:
gun control system complementary sliding modes self-organizing neural network Lyapunovstability
PACS:
TP183
DOI:
10.15938/j.emc.2018.06.013
Abstract:
 A self-organizing neural network with complementary sliding modes control strategy is proposedfor the strong nonlinearities and uncertainties of a gun control system ( GCS),
which consists of the self-organizing neural network controller ( SNNC) and the auxiliary compensation controller ( ACC) with thecomplementary sliding mode surface. The self-organizing neural network controller included a Hermitepolynomial,a variable structure self-organizing neural network ( VSSNN ) and self-learning parameterswith the gradient descent method,which reduced the computational complexity and accelerated the abilityof adaptation. The gradient descent method adjusted parameters of the neural network and promoted theconvergence rapidity. The auxiliary compensator was introduced to further reduce steady-state error of thesystem,which satisfied the basic indicators of requirements and guaranteed the stability and robustness ofthe system in the sense of Lyapunov. The semi-physical test simulation shows that the control strategygreatly improves the control accuracy and robustness of the system,and effectively eliminates the influ-ence of disturbance in the system.

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Last Update: 2018-07-02