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<p>To address the challenges of multi-variable coupling and strong nonlinear constraints in trajectory optimization for high-speed boost-glide flight test vehicles, this paper proposes a dual-layer iterative optimization framework based on Improved Adaptive Particle Swarm Optimization-Nonlinear Programming (IAPSO-NLP). A parametric optimal model is established to reduce the search space, and the PSO algorithm is enhanced by incorporating a dynamic evolution index and elite-guided neighborhood searching strategy, thereby developing the IAPSO algorithm with superior global exploration efficiency. Integrated with a local optimization layer utilizing sequence quadratic programming, a hierarchical framework is formulated to effectively balance global convergence and constraint satisfaction accuracy. The proposed methodology offers a novel approach for multi-stage trajectory optimization under stringent constraints, demonstrating substantial engineering potential for energy management and maneuverability enhancement in high-speed vehicles.</p>

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A Skip Trajectory Optimization Method for High-Speed Boost-Glide Flight Test Vehicles Based on IAPSO-NLP

  • 期刊论文

  • 发表日期：

    2026年2月23日

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  • Xinyu Liu,
  • Zhe Liu,
  • Yuelong He,
  • Guodong Cai

摘要

To address the challenges of multi-variable coupling and strong nonlinear constraints in trajectory optimization for high-speed boost-glide flight test vehicles, this paper proposes a dual-layer iterative optimization framework based on Improved Adaptive Particle Swarm Optimization-Nonlinear Programming (IAPSO-NLP). A parametric optimal model is established to reduce the search space, and the PSO algorithm is enhanced by incorporating a dynamic evolution index and elite-guided neighborhood searching strategy, thereby developing the IAPSO algorithm with superior global exploration efficiency. Integrated with a local optimization layer utilizing sequence quadratic programming, a hierarchical framework is formulated to effectively balance global convergence and constraint satisfaction accuracy. The proposed methodology offers a novel approach for multi-stage trajectory optimization under stringent constraints, demonstrating substantial engineering potential for energy management and maneuverability enhancement in high-speed vehicles.