In order to cope with the development trend of informationization and clustering in modern battlefields

this paper studies the reentry flight time constraint in the mission of multi-hypersonic vehicles’ saturation attack

and proposes a time-controllable reentry guidance law based on the Deep Q-learning Network(DQN). Its workflow is mainly divided into three parts. First

according to the current flight state and angle of attack vs.velocity profile

the amplitude of bank angle is planned based on the prediction-correction module of longitudinal trajectory. Then the online constraint enhanced management module performs safety limiting processing the amplitude of bank angle. Finally

based on Markov Decision Process modeling of lateral flight state

the symbol planning module aims to adjust reentry flight time and to designs the corresponding deep neural network for offline training to generate the symbol of bank angle online

and then amplitude information is combined to form the final bank angle command. The comparative analysis of mutiple simulations shows that in multi-mission simulation under nominal environment

the time-controllable reentry guidance law can independently plan bank angle’s symbol online and adjust reentry flight time to meet different mission requirements without affecting guidance precision. In the Monte Carlo simulations with biased parameters

the time error can still be controlled within a reasonable range while ensuring safe and stable reentry flight. Therefore

compared with the traditional method

the reentry guidance law designed in this paper has good performance in terms of task adaptability

robustness

and time controllability

and it can effectively meet reentry mission requirements with the flight-time constraint.