Neural network assisted inverse dynamic guidance for terminally constrained entry flight.
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.
School of Astronautics, Beihang University, Beijing 100191, China.
Hao
0000-0002-4417-4438
H
Zhou
School of Astronautics, Beihang University, Beijing 100191, China.
Tawfiqur
0000-0002-4919-2782
T
Rahman
School of Astronautics, Beihang University, Beijing 100191, China.
Wanchun
W
Chen
D000465:Algorithms; D008962:Models, Theoretical; D016571:Neural Networks, Computer