Research on Intelligent Yaw Control of Roadheader

DOI

10.2991/eame-17.2017.32How to use a DOI?

Keywords

optimal yaw velocity; intelligent control; neural network; residual analysis; feasible margin

Abstract

Under actual operating conditions, the yaw velocity of boom roadheader has direct influence on the efficiency and reliability of boom roadheader. The PID control method which uses neural network establish the intelligent control system of boom roadheader yaw, based on the MATLAB surface fitting technique, the relationship between the coefficient of coal and rock ƒ, the cutting depth B and the yaw velocity V is obtained. The optimal yaw velocity generation module is established by using Simulink neural network, through simulation and residual analysis, it is found that the maximum residual value of the yaw velocity and optimal yaw velocity is within the feasible margin, it can achieve the accurate tracking of yaw velocity to the optimal yaw velocity and the automatic control of roadheader.

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/).

Download article (PDF)

TY  - CONF
AU  - Renjin Feng
AU  - Liangying Hao
AU  - Dongmin Gai
AU  - Xunan Liu
AU  - Jincheng Wang
AU  - Yang Zhao
AU  - Long Luo
PY  - 2017/04
DA  - 2017/04
TI  - Research on Intelligent Yaw Control of Roadheader
BT  - Proceedings of the 2017 2nd International Conference on Electrical, Automation and Mechanical Engineering (EAME 2017)
PB  - Atlantis Press
SP  - 132
EP  - 135
SN  - 2352-5401
UR  - https://doi.org/10.2991/eame-17.2017.32
DO  - 10.2991/eame-17.2017.32
ID  - Feng2017/04
ER  -