Autonomous Descent Guidance via Sequential Pseudospectral Convex Programming

Abstract

AbstractThis 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.