Research on real-time reachability evaluation for reentry vehicles based on fuzzy learning

Abstract Accurate and rapid prediction of reentry trajectory and landing point is the basis to ensure the reentry vehicle recovery and rescue, but it has high requirements for the continuity and stability of real-time monitoring and positioning data and the fidelity of the reentry prediction model. In order to solve the above contradiction, based on the theory of relative entropy and closeness in fuzzy learning, research on real-time evaluation of reentry reachability is presented in this article. With the Monte Carlo analysis data during the design and evaluation of the reentry vehicle control system, the reentry trajectory feature information base is designed. With the matching identification decision strategy between the identified trajectory and trajectory feature base, the reachability of the reentry vehicle, reachable trajectory, and landing point can be predicted. The simulation results show that by reasonably selecting the time window and using the evaluation method designed in this article, making statistics of the trajectory sequence number and frequency identified based on relative entropy and closeness method, the reachability evaluation results can be given stably, which is suitable for the real-time task evaluation of TT&C system.