| Issue |
MATEC Web Conf.
Volume 139, 2017
2017 3rd International Conference on Mechanical, Electronic and Information Technology Engineering (ICMITE 2017)
|
|
|---|---|---|
| Article Number | 00168 | |
| Number of page(s) | 4 | |
| DOI | https://doi.org/10.1051/matecconf/201713900168 | |
| Published online | 05 December 2017 | |
A Convolutional Neural Networks Approach to Devise Controller
1 Huazhong University of Science and Technology
2 School of Automation
* e-mail: lxd@hust.edu.cn
** e-mail: dyl@hust.edu.cn
PID controller is widely used in many fields. The input sequence and output sequence of a well-parameterized PID controller are transformed into a matrix presented in images in this paper, and a Every-Time step data augmentation algorithm is operated. This paper propose a image to image Convolutional Neural Networks(CNN) structure to learn the PID controller, which incorporates the successful methods in computer vision and deep learning to control field. The simulation is performed using Matlab/Simulink and Keras[1]. The simulation demonstrates that the CNN controller is capable in performance.
Key words: PID controller / CNN / Deep learning / Control
© The Authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (http://creativecommons.org/licenses/by/4.0/).
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