Reentry Trajectory Online Planning and Guidance Method Based on TD3

Aiming at the problem of poor autonomy and weak time performance of reentry trajectory planning for Reusable Launch Vehicle (RLV), an online reentry trajectory planning and guidance method based on Twin Delayed Deep Deterministic Policy Gradient (TD3) is proposed. In view of the advantage that the drag acceleration can be quickly measured by the airborne inertial navigation equipment, the reference profile adopts the design of the drag acceleration–velocity profile in the reentry corridor. In order to prevent the problem of trajectory angle jump caused by the unsmooth turning point of the section, the section form adopts the form of four multiple functions to ensure the smooth connection of the turning point. Secondly, considering the advantages of the TD3 dual Critic network structure and delay update mechanism to suppress strategy overestimation, the TD3 algorithm framework is used to train multiple strategy networks offline and output profile parameters. Finally, considering the reentry uncertainty and the guidance error caused by the limitation of the bank angle reversal amplitude during lateral guidance, the networks are invoked online many times to solve the profile parameters in real time and update the profile periodically to ensure the rapidity and autonomy of the guidance command generation. The TD3 strategy networks are trained offline and invoked online many times so that the cumulative error in the previous guidance period can be eliminated when the algorithm is called again each time, and the online rapid generation and update of the reentry trajectory is realized, which effectively improves the accuracy and computational efficiency of the landing point.