Reentry guidance is one of the key technologies in hypersonic vehicle research field. In addition to the constraints on its
final position coordinates, the vehicle must also impact the target from a specified direction with high precision. And
therefore the adaptability of guidance law is critical to control the velocity of hypersonic vehicle and firing accuracy
properly in different surroundings of large airspace. In this paper, a new adaptive guidance strategy based on Back
Propagation (BP) neural network for the reentry mission of a generic hypersonic vehicle is presented. Depending on the
nicer self-learn ability of BP neural network, the guidance law considers the influence of biggish mis-modeling of
aerodynamics, structure error and other initial disturbances on the flight capability of vehicle. Consequently, terminal
position accuracy and velocity are guaranteed, while many constraints are satisfied. Numerical simulation results clearly
bring out the fact that the proposed reentry guidance law based on BP neural network is rational and effective.
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