在火箭故障的情况下，导航误差、模型参数偏差、故障辨识偏差等共同构成火箭制导中需要考虑的不确定性干扰因素，传统的轨迹规划方法将火箭抵抗各种不确定性干扰的能力完全交给跟踪控制回路实现，这大大增加了跟踪控制回路的设计难度和压力，难以实现火箭轨迹的鲁棒制导与最优制导的协调。在火箭故障的情况下，为了进一步保证火箭的入轨安全，提升任务成功率，对火箭重构规划的随机轨迹优化方法进行了研究。通过系统协方差的演化过程描述了随机因素扰动对系统状态的影响，根据系统终端协方差建立了随机因素影响下的入轨安全性约束条件，基于前馈-反馈结合闭环控制率以及单纯前馈的控制方式构建了两种随机性的火箭弹道重构问题以提升规划结果对随机扰动的鲁棒性。为了提升故障触发后重规划算法的求解性能，采用了相应的凸化方法进行问题非线性项的处理，提出了序列凸化方法实现对上述问题的求解。仿真结果表明所提出的方法保证了规划结果在不确定扰动下的轨道安全高度，同时前馈-反馈的控制方式能够有效控制随机扰动对系统状态的影响，而确定性方法在偏差较大的情况下会导致火箭坠毁。

In the event of rocket failures, navigation errors, model parameter deviations and fault identification biases jointly constitute significant sources of uncertainty in rocket guidance. Conventional trajectory planning methods largely rely on the tracking control loop to compensate for such uncertainties, which substantially increases the design complexity and burden of the control system, making it difficult to achieve a proper balance between robust guidance and optimal guidance performance. To further ensure orbital insertion safety and improve mission success probability under failure conditions, this paper investigates a stochastic trajectory optimization method for rocket trajectory reconstruction. The effects of random disturbances on system states are characterized through the evolution of system covariance, based on which orbital safety constraints under uncertainty are formulated using terminal state covariance. Two stochastic trajectory reconstruction problems are established to enhance robustness against random disturbances: one employing a combined feedforward–feedback closed-loop control law, and the other based on a purely feedforward control strategy. To enhance the solution performance of fault-triggered re-planning, appropriate convexification techniques are introduced to handle nonlinear terms, and a sequential convexification approach is proposed to solve the resulting optimization problems. Simulation results demonstrate that the proposed method guarantees orbital safety altitude under uncertain disturbances. Moreover, the feedforward–feedback control strategy effectively suppresses the impact of random disturbances on system states, whereas deterministic methods may lead to rocket crash in the presence of large deviations.