Proceedings of the 2017 2nd International Conference on Electrical, Automation and Mechanical Engineering (EAME 2017)

The Application of BP Neural Network in the Thrust Hydraulic System for Shield Tunneling Machine

Authors
Geqiang Li, Yinting Ding, Kui Chen, Weifeng Han, Bingjing Guo
Corresponding Author
Geqiang Li
Available Online April 2017.
DOI
10.2991/eame-17.2017.39How to use a DOI?
Keywords
shield machine; thrust hydraulic system; BP neural network; simulation
Abstract

This paper studies the problem of the precision of thrust speed and pressure compound control under the uncertain external load condition for shield tunneling machine. An controller about thrust speed and pressure is designed based on the traditional PID control algorithm combined with BP neural networks. By using AMEsim and Simulink software, the physical model and controller of the thrust system are established and joint simulation is conducted. The thrust system of shield machine model is simulated under the condition of different load and flow, which proves the stability and robustness of the controller with BP neural network algorithm.

Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2017 2nd International Conference on Electrical, Automation and Mechanical Engineering (EAME 2017)
Series
Advances in Engineering Research
Publication Date
April 2017
ISBN
10.2991/eame-17.2017.39
ISSN
2352-5401
DOI
10.2991/eame-17.2017.39How to use a DOI?
Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Geqiang Li
AU  - Yinting Ding
AU  - Kui Chen
AU  - Weifeng Han
AU  - Bingjing Guo
PY  - 2017/04
DA  - 2017/04
TI  - The Application of BP Neural Network in the Thrust Hydraulic System for Shield Tunneling Machine
BT  - Proceedings of the 2017 2nd International Conference on Electrical, Automation and Mechanical Engineering (EAME 2017)
PB  - Atlantis Press
SP  - 160
EP  - 163
SN  - 2352-5401
UR  - https://doi.org/10.2991/eame-17.2017.39
DO  - 10.2991/eame-17.2017.39
ID  - Li2017/04
ER  -