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高超声速飞行器再入飞行过程中,需要满足多种过程约束和终端状态约束,同时再入初始状态根据飞行任务不同会有较大变化,针对其特点的快速轨迹优化问题已成为当今热点。本文研究了一种基于“初值轨迹生成+Gauss伪谱法+SQP求解NLP”的方法,既利用了Gauss伪谱法收敛快、精度高的特点,又结合初值轨迹生成算法,弥补了Gauss伪谱法对初值敏感的不足。本文在仿真过程中选取再入总吸热量最小为性能指标,求解了满足多种约束的再入轨迹,并将优化的结果与数值积分的结果进行比较,验证了此算法有效性和可行性。
Hypersonic vehicle re-entry flight process, the need to meet a variety of process constraints and terminal state constraints, and re-enter the initial state, according to the different missions will have a greater change, the rapid trajectory optimization for its characteristics has become a hot issue today. In this paper, a method based on “initial trajectory generation + Gauss pseudo-spectral + SQP for solving NLP ” is studied in this paper. It not only makes use of the fast convergence and high precision of Gauss pseudospectral method, but also combines the initial trajectory generation algorithm Gauss pseudo-spectral sensitivity of the initial lack of. In this paper, we choose the minimum reentrant total heat absorption as the performance index in the simulation process, and solve the reentry trajectory satisfying various constraints. The results of the optimization and numerical integration are compared to verify the effectiveness and feasibility of this algorithm .