This chapter expands our development of an autonomous descent guidance algorithm which is able to deal with both the aerodynamic descent and the powered landing phases of a reusable rocket. The method uses sequential convex optimization applied to a Cartesian representation of the equations of motion, and the transcription is based on the use of hp pseudospectral methods. The major contributions of the formulation are a more systematic exploitation and separation of convex and non-convex contributions to minimize the computation of the latter, the inclusion of highly nonlinear terms represented by aerodynamic accelerations, a complete reformulation of the problem based on the use of Euler angle rates as control means, an improved transcription based on the use of a generalized hp pseudospectral method, and a dedicated formulation of the aerodynamic guidance problem for reusable rockets. The approach is demonstrated for a 40 kN-class reusable rocket. Numerical results confirm that the methodology we propose is very effective and able to satisfy all the constraints acting on the system. It is therefore a valid candidate solution to solve the entire descent phase of reusable rockets in real-time.
Autonomous Descent Guidance via Sequential Pseudospectral Convex Programming
Springer ser. in Astrophysics & Cosmology
Autonomous Trajectory Planning and Guidance Control for Launch Vehicles ; Kapitel : 5 ; 129-176
2023-04-16
48 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Pseudospectral Convex Optimization for Powered Descent and Landing
Online Contents | 2017
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