Six-Degree-of-Freedom Rocket Landing Optimization via Augmented Convex–Concave Decomposition

What is it about?

we propose a new methodology to deal with non-convex equality constraints obeying mild assumptions. By using exact-penalty theory we can relax the non-convex equality constraint into a set of four inequalities, leading to a more robust and interpretable sequential convex algorithm. The proposed technique can also be used to model discrete decision-making processes, such as on-off of actuators. The methodology is applied to a six-degrees-of-freedom rocket landing problem.

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