Real-Time Optimal Approach and Capture of ENVISAT Based on Neural Networks
Joint Authors
Dong, Yunfeng
Li, Hongjue
Li, Peiyun
Source
International Journal of Aerospace Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-17, 17 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-01-30
Country of Publication
Egypt
No. of Pages
17
Abstract EN
A neural network-based controller is developed to enable a chaser spacecraft to approach and capture a disabled Environmental Satellite (ENVISAT).
This task is conventionally tackled by framing it as an optimal control problem.
However, the optimization of such a problem is computationally expensive and not suitable for onboard implementation.
In this work, a learning-based approach is used to rapidly generate the control outputs of the controller based on a series of training samples.
These training samples are generated by solving multiple optimal control problems with successive iterations.
Then, Radial Basis Function (RBF) neural networks are designed to mimic this optimal control strategy from the generated data.
Compared with a traditional controller, the neural network controller is able to generate real-time high-quality control policies by simply passing the input through the feedforward neural network.
American Psychological Association (APA)
Li, Hongjue& Dong, Yunfeng& Li, Peiyun. 2020. Real-Time Optimal Approach and Capture of ENVISAT Based on Neural Networks. International Journal of Aerospace Engineering،Vol. 2020, no. 2020, pp.1-17.
https://search.emarefa.net/detail/BIM-1168222
Modern Language Association (MLA)
Li, Hongjue…[et al.]. Real-Time Optimal Approach and Capture of ENVISAT Based on Neural Networks. International Journal of Aerospace Engineering No. 2020 (2020), pp.1-17.
https://search.emarefa.net/detail/BIM-1168222
American Medical Association (AMA)
Li, Hongjue& Dong, Yunfeng& Li, Peiyun. Real-Time Optimal Approach and Capture of ENVISAT Based on Neural Networks. International Journal of Aerospace Engineering. 2020. Vol. 2020, no. 2020, pp.1-17.
https://search.emarefa.net/detail/BIM-1168222
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1168222