Learning-based interfered fluid avoidance guidance for hypersonic reentry vehicles with multiple constraints

Abstract

To address the problem of no-fly zone avoidance for hypersonic reentry vehicles in the multiple constraints gliding phase, a learning-based avoidance guidance framework is proposed. First, the reference heading angle determination problem is solved efficiently and skillfully by introducing a nature-inspired methodology based on the concept of the interfered fluid dynamic system (IFDS), in which the distance and relative position relationships of all no-fly zones can be comprehensively considered, and additional rules are no longer needed. Then, by incorporating the predictor–corrector method, the heading angle corridor, and bank angle reversal logic, a fundamental interfered fluid avoidance guidance algorithm is proposed to steer the vehicle toward the target zone while avoiding no-fly zones. In addition, a learning-based online optimization mechanism is used to optimize the IFDS parameters in real time to improve the avoidance guidance performance of the proposed algorithm in the entire gliding phase. Finally, the adaptability and robustness of the proposed guidance algorithm are verified via comparative and Monte Carlo simulations.