Atmospheric powered descent guidance can be solved by successive convexification; however, its onboard application is impeded by the sharp increase in computation caused by nonlinear aerodynamic forces. The problem has to be converted into a sequence of convex subproblems instead of a single convex problem when aerodynamic forces are ignored. Besides, each subproblem is significantly more complicated, which increases computation. A fast real-time interior point method was presented to solve the correlated convex subproblems onboard in the work. The main contributions are as follows: Firstly, an algorithm was proposed to accelerate the solution of linear systems that cost most of the computation in each iterative step by exploiting the specific problem structure. Secondly, a warm-starting scheme was introduced to refine the initial value of a subproblem with a rough approximate solution of the former subproblem, which lessened the iterative steps required for each subproblem. The method proposed reduced the run time by a factor of 9 compared with the fastest publicly available solver tested in Monte Carlo simulations to evaluate the efficiency of solvers. Runtimes on the order of 0.6 s are achieved on a radiation-hardened flight processor, which demonstrated the potential of the real-time onboard application.

翻译：大气动力下降制导可通过逐次凸化方法求解，然而其星载应用受限于非线性气动力导致的计算量剧增。该问题必须转化为一系列凸子问题而非忽略气动力时的单一凸问题。此外，每个子问题的复杂度显著增加，进一步加剧了计算负担。本文提出了一种快速实时内点法，用于星载求解相关凸子问题。主要贡献如下：首先，通过利用特定问题结构，提出了一种加速线性系统求解的算法，该步骤占据每次迭代计算量的主要部分。其次，引入一种热启动方案，利用前一个子问题的粗略近似解优化当前子问题的初始值，从而减少每个子问题所需的迭代步数。与蒙特卡洛模拟中测试的最快公开求解器相比，所提方法将运行时间缩短了9倍。在抗辐射飞行处理器上实现了约0.6秒的运行时长，验证了其在实时星载应用中的潜力。