  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.
