Staring Imaging Real-Time Optimal Control Based on Neural Network
Joint Authors
Dong, Yunfeng
Li, Hongjue
Li, Peiyun
Source
International Journal of Aerospace Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-11-17
Country of Publication
Egypt
No. of Pages
14
Abstract EN
In this paper, a real-time optimal attitude controller is designed for staring imaging, and the output command is based on future prediction.
First, the mathematical model of staring imaging is established.
Then, the structure of the optimal attitude controller is designed.
The controller consists of a preprocessing algorithm and a neural network.
Constructing the neural network requires training samples generated by optimization.
The objective function in the optimization method takes the future control effect into account.
The neural network is trained after sample creation to achieve real-time optimal control.
Compared with the PID (proportional-integral-derivative) controller with the best combination of parameters, the neural network controller achieves better attitude pointing accuracy and pointing stability.
American Psychological Association (APA)
Li, Peiyun& Dong, Yunfeng& Li, Hongjue. 2020. Staring Imaging Real-Time Optimal Control Based on Neural Network. International Journal of Aerospace Engineering،Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1168289
Modern Language Association (MLA)
Li, Peiyun…[et al.]. Staring Imaging Real-Time Optimal Control Based on Neural Network. International Journal of Aerospace Engineering No. 2020 (2020), pp.1-14.
https://search.emarefa.net/detail/BIM-1168289
American Medical Association (AMA)
Li, Peiyun& Dong, Yunfeng& Li, Hongjue. Staring Imaging Real-Time Optimal Control Based on Neural Network. International Journal of Aerospace Engineering. 2020. Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1168289
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1168289