动态博弈下变后掠翼飞行器智能决策规避方法

Intelligent evasion maneuvering decision method for a variable-sweep wing aircraft under dynamic game conditions

关键词:  变体飞行器  强化学习  自主变形决策  柔性动作评价  规避

Key words:  morphing aircraft  reinforcement learning  autonomous morphing decision-making  soft actor-critic (SAC)  evasion

摘要:

为解决变后掠翼飞行器在动态拦截环境下的自主规避问题,本文提出一种智能变形决策算法,通过实时调节后掠角,将动态变形作为规避的核心手段。首先,针对后掠角可变的变体飞行器模型,基于最小二乘法拟合气动系数,并分析了气动参数对飞行器气动性能的影响,从而为智能变形决策提供依据。其次,考虑变后掠翼飞行器飞行速度、飞行区域边界等实际物理约束条件,构建面向突防任务的变体飞行器双拦截器动态博弈场景,结合飞行器状态、拦截器状态及目标信息的状态空间,设计以规避效果、气动性能为优化目标的决策模型。仿真实验验证结果表明,本文算法能够在完成自主变形决策规避的同时,兼顾机动性和敏捷性,克服了传统变形策略依赖离线优化计算和根据预设任务切换,难以自适应应对高动态博弈环境的局限性。

Abstract:

To address the autonomous evasion problem for variable-sweep wing aircraft in dynamic intercept environments, this paper proposes an intelligent morphing decision algorithm. This algorithm leverages dynamic morphing, primarily through real-time adjustment of the sweep angle, as the core evasion strategy.Initially, aerodynamic coefficients for the variable-sweep-angle aircraft model are fitted using the least-squares method. The influence of these aerodynamic parameters on the aircraft′s performance is then analyzed, providing the foundation for intelligent morphing decision-making. Subsequently, a dynamic game scenario is developed for a penetration mission involving the morphing aircraft and dual interceptors, incorporating practical physical constraints such as flight speed and operational area boundaries. A decision model is then designed, integrating a state space that includes aircraft status, interceptor status, and target information, with the optimization objectives of maximizing evasion effectiveness and aerodynamic performance.Finally, simulation results demonstrate that the proposed algorithm successfully achieves autonomous morphing-based evasion while maintaining high maneuverability and agility. This approach overcomes the limitations of traditional morphing strategies, which relay on offline optimization and predefined task switching, making it difficult to adapt to highly dynamic game environments.