Six-Degrees-of-Freedom Rocket Landing Optimization by Augmented Convex-Concave Decomposition

In this paper an Augmented Convex-Concave Decomposition (ACCD) method for treating nonlinear equality constraints in an otherwise convex problem is proposed. This augmentation improves greatly the feasibility of the problem when compared to the original convex-concave decomposition approach. The effectiveness of the ACCD is demonstrated by solving a 6-DoF rocket landing problem, subject to multiple nonlinear equality constraints. Compared with known approaches such as standard Sequential Convex Programming, it is shown that the proposed ACCD leads to a more robust methodology for the computation of optimal solutions, and to a much more interpretable behavior of the sequential convex algorithm. Numerical results are shown for a representative, reusable rocket benchmark problem.