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Updated: Aug 2, 2025

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

Tiancai Wu1, Honglun Wang2, Yiheng Liu1

  • 1School of Automation Science and Electrical Engineering, Beihang University, 100191, Beijing, China; Shenyuan Honors College of Beihang University, 100191, Beijing, China; The Science and Technology on Aircraft Control Laboratory, Beihang University, 100191, Beijing, China.

ISA Transactions
|April 19, 2023
PubMed
Summary

A new guidance framework helps hypersonic vehicles avoid no-fly zones during reentry. It uses an interfered fluid dynamic system (IFDS) and online learning for robust, adaptive flight path control.

Keywords:
Deep reinforcement learningHypersonic reentry vehicleInterfered fluid dynamic system (IFDS)No-fly zonePredictor–corrector guidanceReentry avoidance guidance

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Area of Science:

  • Aerospace Engineering
  • Guidance, Navigation, and Control (GNC)

Background:

  • Hypersonic reentry vehicles face complex challenges in avoiding restricted airspace.
  • Existing guidance methods struggle with multiple constraints during the gliding phase.

Purpose of the Study:

  • To propose a learning-based avoidance guidance framework for hypersonic reentry vehicles.
  • To ensure safe navigation through no-fly zones during the gliding phase.

Main Methods:

  • Introduced a nature-inspired interfered fluid dynamic system (IFDS) for reference heading angle determination.
  • Developed a guidance algorithm using predictor-corrector, heading angle corridors, and bank angle reversal.
  • Implemented a learning-based online optimization mechanism for real-time IFDS parameter tuning.

Main Results:

  • The IFDS comprehensively considers no-fly zone positions without additional rules.
  • The guidance algorithm effectively steers vehicles toward targets while avoiding restricted areas.
  • Online optimization significantly improved avoidance performance throughout the gliding phase.

Conclusions:

  • The proposed framework demonstrates adaptability and robustness in no-fly zone avoidance.
  • The learning-based approach offers enhanced performance for hypersonic vehicle guidance.
  • Comparative and Monte Carlo simulations validated the algorithm's effectiveness.