针对可重复使用运载器末端能量管理阶段的在线轨迹生成与制导问题，研究了一种基于参数化轨迹描述且不依赖在线积分推演与气动辨识的三维轨迹预测-校正制导算法。首先，设计了由动压上边界、下边界和最大能量边界构成的动压包线，由一个参数对包线内的动压剖面进行描述，采用离线计算的方式预先获得飞行航程随动压剖面参数、倾侧角和能量高度变化的关系并存为三维数表。随后，根据当前状态和地面航迹参数计算得到各飞行阶段地面航程信息，在待飞航程的预测中，考虑侧向机动的航程损失和模型偏差影响，采用分段查表和在线估计航程修正系数的方法对预测航程进行了两次修正。最后，研究了约束条件下的多轨迹参数连续更新策略，以保证消除航程偏差的同时轨迹具有适宜性。仿真结果表明，该方法对于初始位置、能量状态散布不敏感，其末端位置控制精度保持在米级。完成单次轨迹预测-校正的时间不超过2.3 ms，拥有较高的在线预测效率，对突发故障造成的模型偏差具有较强的适应能力。
To address the in-flight trajectory generation and the guidance of reusable launch vehicles in the terminal phase of area energy management, a three-dimensional predictor-corrector guidance algorithm that does not rely on on-line integral prediction and aerodynamic identification is studied based on the parameterized trajectory description. Firstly, the dynamic pressure envelope consisting of the upper and lower dynamic pressure boundaries and the maximum energy boundary is designed, and the dynamic pressure profile generated in the envelope is described by a single parameter. By using the pre-calculation method, the relationship of flight range with the dynamic pressure profile parameter, the bank angle and the energy height is obtained and saved as a three-dimensional data table. Then, according to the current state and ground track parameters, the ground track range of each flight stage is calculated. In the prediction of the range-to-go, to address the range loss of the lateral maneuver and the influence of model deviation, the more accurate value of the range-to-go is obtained by combining the subsection look-up table method and the on-line estimation of the range correction coefficient. Lastly, to ensure the fitness of trajectory while eliminating the range deviation, the continuous updating strategy of multiple trajectory parameters under constraint conditions is studied The simulation results show that the method is insensitive to the initial position and energy distribution, and the accuracy of the terminal position is kept at the meter level. One cycle of trajectory prediction and correction takes no more than 2.3 ms on a PC, indicating a high efficiency of the on-line guidance method. The test for the failure mode illustrates a strong adaptability of the proposed algorithm.
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