A Fast Algorithm for Onboard Atmospheric Powered Descent Guidance

Atmospheric powered descent guidance (APDG) can be solved by successive convexification; however, its onboard application is impeded by high computational cost. When aerodynamic forces are ignored, powered descent guidance (PDG) can be converted to a single convex problem. In contrast, APDG has to be converted into a sequence of convex subproblems, each of which is significantly more complicated. Consequently, the computation increases sharply. A fast real-time interior point method was presented to solve the correlated convex subproblems efficiently onboard in the work. The main contributions are as follows: First, 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. Second, 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.