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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Qi Wang He Liang Lu Shan Yang Guang Sun Jun |
| Copyright Year | 2013 |
| Description | Author affiliation: Shanghai Aerosp. Control Eng. Inst., Shanghai, China (Qi Wang; He Liang; Lu Shan; Yang Guang; Sun Jun) |
| Abstract | Reusable Booster Vehicles(RBV) is a new type of Two-Stage-To-Orbit(TSTO) partly reusable launch vehicle, which can greatly reduce launch cost. The guidance control during the reentry period of RBV is critical for safe and reliable return. In order to satisfy special task demands, this paper has analyzed the performance of neural network guidance, normal trajectory guidance and prediction-correction guidance, and given related comparison conclusions. The neural network has been trained according to the trajectory parameters generated by using prediction-correction guidance method. The state parameters of RBV have been used as the input, and the bank angle has been the output. Simulation result has indicated that the proposed method has good real time performance and its computation occupation is low. What's more, it also has good robustness and adaption performance by using the proposed method. In the case of larger initial bias or greater error of RBV pneumatic parameters, it could attain good land precision. It has a very good application future by utilizing the reentry guidance method for RBV based on neural network. |
| Starting Page | 3360 |
| Ending Page | 3364 |
| File Size | 747230 |
| Page Count | 5 |
| File Format | |
| ISBN | 9789881563835 |
| ISSN | 19341768 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2013-07-26 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | TCCT, CAA |
| Subject Keyword | Neural Network Reentry Neural networks Trajectory Electronic mail Reusable Booster Vehicle MATLAB Sun Vehicles Aerospace control |
| Content Type | Text |
| Resource Type | Article |
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