Reference trajectory re-entry guidance without pre-launch data storage

1965 ◽  
Author(s):  
J. CARTER
Keyword(s):  
Data Storage ◽  
Reference Trajectory ◽  
Entry Guidance

Reference trajectory re-entry guidance without prelaunch data storage.

1965 ◽  
Vol 2 (6) ◽  
pp. 967-970 ◽  
Author(s):  
JOHN P. CARTER ◽  
LAWRENCE D. PERLMUTTER
Keyword(s):  
Data Storage ◽  
Reference Trajectory ◽  
Entry Guidance

scholarly journals Neural Network Assisted Inverse Dynamic Guidance for Terminally Constrained Entry Flight

2014 ◽  
Vol 2014 ◽  
pp. 1-12
Author(s):  
Hao Zhou ◽  
Tawfiqur Rahman ◽  
Wanchun Chen
Keyword(s):  
Neural Network ◽  
Terminal Velocity ◽  
Bézier Curve ◽  
Bezier Curve ◽  
Reference Trajectory ◽  
Inverse Dynamic ◽  
Guidance Law ◽  
Terminal Constraints ◽  
Entry Guidance ◽  
Improved Performance

This paper presents a neural network assisted entry guidance law that is designed by applying Bézier approximation. It is shown that a fully constrained approximation of a reference trajectory can be made by using the Bézier curve. Applying this approximation, an inverse dynamic system for an entry flight is solved to generate guidance command. The guidance solution thus gotten ensures terminal constraints for position, flight path, and azimuth angle. In order to ensure terminal velocity constraint, a prediction of the terminal velocity is required, based on which, the approximated Bézier curve is adjusted. An artificial neural network is used for this prediction of the terminal velocity. The method enables faster implementation in achieving fully constrained entry flight. Results from simulations indicate improved performance of the neural network assisted method. The scheme is expected to have prospect for further research on automated onboard control of terminal velocity for both reentry and terminal guidance laws.

Neural-network-based real-time trajectory replanning for Mars entry guidance

2016 ◽  
Vol 231 (14) ◽  
pp. 2634-2645
Author(s):  
X Zheng ◽  
H Huang ◽  
W Li
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Trajectory Optimization ◽  
Reference Trajectory ◽  
The Real ◽  
The Neural Network ◽  
Entry Guidance ◽  
Computational Speed ◽  
Uncertain Factor ◽  
Predictor Corrector

The real-time trajectory replanning method which is used for the guidance of Mars entry is investigated in this paper. Comparing with the traditional Mars entry guidance methods, such as the reference-trajectory tracking guidance and predictor–corrector guidance, the real-time trajectory replanning method can increase the reliability of the mission remarkably. When faults occur during the Mars entry phase, a replacement trajectory will be planned quickly. Due to the limited onboard computing capacity, replanning the trajectory onboard is a challenging task. Corresponding to this problem, the neural network is trained to approximate the dynamics of the atmospheric entry. The uncertain factor of the atmospheric density is also included in the neural network. Then, by using the characters of the neural network, the analytical expressions of the Jacobian which are needed in trajectory optimization are derived. Finally, an estimation-replanning guidance procedure is introduced. The numerical simulation shows that the proposed guidance strategy can decrease the error of final states effectively, and the neural network approximation improves the computational speed of the nonlinear programming solver remarkably, which makes the method more suitable for use onboard.

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