A Convolutional Neural Networks Approach to Devise Controller
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 Convolutiona...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2017-01-01
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| Series: | MATEC Web of Conferences |
| Subjects: | |
| Online Access: | https://doi.org/10.1051/matecconf/201713900168 |
| Summary: | 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. |
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| ISSN: | 2261-236X |