Proceedings of the 2016 6th International Conference on Management, Education, Information and Control (MEICI 2016)

Ball Mill Automatic Control System Design Based on Particle Swarm Optimization Algorithm

Authors
Li Ai, Yan Xiong
Corresponding Author
Li Ai
Available Online September 2016.
DOI
10.2991/meici-16.2016.66How to use a DOI?
Keywords
Ball mill; Particle swarm optimization (PSO); Neural network; PID control; Constant power
Abstract

For ball mill grinding process random interference by many factors, processes complex mechanism, there was a big inertia and lag, conventional PID control effect was poor, the particle swarm optimization neural network approach was introduced into the mill control system, it had strong robustness, can effectively overcome the mill main motor power nonlinear, time-varying factors such as interference. System was reliable, adjust speed, anti-interference ability, can better achieve constant power automatic control of the ball mill, with good application value.

Copyright
© 2016, 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 2016 6th International Conference on Management, Education, Information and Control (MEICI 2016)
Series
Advances in Intelligent Systems Research
Publication Date
September 2016
ISBN
978-94-6252-251-0
ISSN
1951-6851
DOI
10.2991/meici-16.2016.66How to use a DOI?
Copyright
© 2016, 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  - Li Ai
AU  - Yan Xiong
PY  - 2016/09
DA  - 2016/09
TI  - Ball Mill Automatic Control System Design Based on Particle Swarm Optimization Algorithm
BT  - Proceedings of the 2016 6th International Conference on Management, Education, Information and Control (MEICI 2016)
PB  - Atlantis Press
SP  - 320
EP  - 323
SN  - 1951-6851
UR  - https://doi.org/10.2991/meici-16.2016.66
DO  - 10.2991/meici-16.2016.66
ID  - Ai2016/09
ER  -