Name
Abstract | ||
|---|---|---|
The research is in an attempt to reduce the ground pressure fluctuations when hard rock TBM tunneling and improve TBM geologic adaptability in complex geological environments. Firstly the theoretical ground pressure model is established through mechanical analysis of gripper supporting system, and the mathematical model of gripper cylinder hydraulic system is built up based on flow laws. Then a ground pressure control strategy is proposed base on probabilistic neural network (PNN) rock recognition and theoretical revision. Finally the performance of ground pressure control method is revealed by simulation. The results show that, the set output ground pressure can reach the supporting demand and adapt the rock type, and the ground pressure can trace the set curve steadily. Therefore, the new control method proves to be useful and practical. |
Year | Venue | Keywords |
|---|---|---|
2015 | International Conference on Control Automation and Information Sciences | TBM,ground pressure,gripper cylinder,PNN rock recognition |
Field | DocType | ISSN |
Hydraulic machinery,Propulsion,Flow (psychology),Pressure control,Control engineering,Ground pressure,Probabilistic neural network,Adaptive control,Engineering,Grippers | Conference | 2475-7896 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yunyi Rao | 1 | 0 | 0.34 |
| Guofang Gong | 2 | 2 | 5.62 |
| Xu Yang | 3 | 0 | 1.35 |
| Jianjun Zhou | 4 | 0 | 0.34 |