分导协同攻击作为突破防空体系的有效手段,其技术核心在于多弹协同轨迹规划能力的优化。针对分导滑翔导弹集群协同打击任务中存在的多维度复杂约束问题,包括导弹分离后导致的初始高度差异、速度偏差与姿态角分散,以及三维空间中动态禁飞区的规避需求。提出一种融合高斯伪谱法与协调变量协同控制的新型轨迹规划方法。通过建立导弹三自由度运动学模型,深度耦合气动参数的非线性变化特性与多约束条件,构建了面向分导滑翔导弹集群协同的多目标轨迹优化模型。该方法采用自适应节点分布策略优化GPM的离散化过程,通过在禁飞区边界区域实施网格加密的方法,有效解决了传统方法在航迹拐点处约束违反率过高的问题。最后通过数值仿真验证,分别说明了该方法能够使分导滑翔导弹在初始高度、初始速度、初始姿态不同时,满足规避禁飞区约束,并以指定末速完成协同打击任务,验证了所提方法的有效性和优越性。

As an effective approach to penetrate air defense systems, fractionated cooperative attack relies fundamentally on optimizing multi-projectile collaborative trajectory planning capabilities. This study addresses the multidimensional complex constraints in coordinated strikes by Distributed Glide Bomb (DGB) clusters, including initial condition disparities resulting from post-separation dynamics (altitude differences, velocity deviations, and attitude dispersion) and three-dimensional dynamic no-fly zone avoidance requirements. A novel trajectory planning methodology is proposed, integrating the Gauss Pseudo spectral Method (GPM) with coordinated-variable control. By establishing a three-degree-of-freedom (3DOF) kinematic model that deeply couples nonlinear aerodynamic parameter variations with multi-constraint conditions (including dynamic equations and geometric constraints), a multi-objective trajectory optimization framework is developed for DGB cluster coordination. The method innovatively employs an adaptive node distribution strategy to optimize the discretization process of the Gauss Pseudo spectral Method (GPM). By implementing mesh refinement in no-fly zone boundary regions, it effectively resolves the issue of high constraint violation rates at trajectory inflection points observed in conventional approaches. Numerical simulations demonstrate that under heterogeneous initial conditions (varying altitudes, velocities, and attitudes), all DGBs successfully generate collision-free 3D trajectories avoiding complex no-fly zones while achieving synchronized strikes with terminal velocities within specified tolerances. The results validate the method’s effectiveness in multi-constraint resolution and its superiority over conventional approaches in computational efficiency and mission adaptability.