Adaptive powered descent guidance based on multi-phase pseudospectral convex optimization

摘要

The real-time and fuel-optimal powered descent guidance is one of the critical technologies for planetary landing. This work focuses on the powered descent guidance algorithm based on pseudospectral convex optimization and aims to increase the accuracy and adaptability of the algorithm. The entire powered descent process is divided into phases according to the thrust switching times, and the pseudospectral method is adopted to discretize the optimization problem. Convexification process is performed to transform the optimization problem into a convex programming problem, and a successive solving process algorithm is proposed. For the established mull-phase pseudospectral convex optimization formulation, the indirect method is adopted to derive and determine the optimal phase division from switching function, and the adaptive mull-phase pseudospectral convex optimization method is proposed. Numerical examples demonstrate that the proposed method has excellent accuracy and adaptability.