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中南大学学报(英文版)

Journal of Central South University

Vol. 23    No. 4    April 2016

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Actuator fault diagnosis of autonomous underwater vehicle based on improved elman neural network
SUN Yu-shan(孙玉山)1,2, LI Yue-ming(李岳明)1,2, ZHANG Guo-cheng(张国成)1,2, ZHANG Ying-hao(张英浩)1,2, WU Hai-bo(吴海波)1,2

1. Science and Technology on Underwater Vehicle Laboratory, Harbin Engineering University, Harbin 150001, China;
2. College of Shipbuilding Engineering, Harbin Engineering University, Harbin 150001, China

Abstract:Autonomous underwater vehicles (AUV) work in a complex marine environment.Its system reliability and autonomous fault diagnosis are particularly important and can provide the basis for underwater vehicles to take corresponding security policy in a failure. Aiming at the characteristics of the underwater vehicle which has uncertain system and modeling difficulty, an improved Elman neural network is introduced which is applied to the underwater vehicle motion modeling. Through designing self-feedback connection with fixed gain in the unit connection as well as increasing the feedback of the output layer node, improved Elman network has faster convergence speed and generalization ability. This method for high-order nonlinear system has stronger identification ability. Firstly, the residual is calculated by comparing the output of the underwater vehicle model (estimation in the motion state) with the actual measured values. Secondly, characteristics of the residual are analyzed on the basis of fault judging criteria. Finally, actuator fault diagnosis of the autonomous underwater vehicle is carried out. The results of the simulation experiment show that the method is effective.

 

Key words: autonomousunderwater vehicle; fault diagnosis; thruster; improved Elman neural network

中南大学学报(自然科学版)
  ISSN 1672-7207
CN 43-1426/N
ZDXZAC
中南大学学报(英文版)
  ISSN 2095-2899
CN 43-1516/TB
JCSTFT
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