Maximum range trajectory optimization for a boost-glide vehicle using adaptive mesh refinement pseudospectral methods

Abstract

The maximum down range trajectory optimization problem with multiple phases and multiple constraints corresponding to the flight of a boost-glide vehicle is considered. The longitudinal motion model was built as a multiphase optimization problem under constraints. Legendre–Gauss–Radau collocation points were used to transcribe the optimization problem into a finite-dimensional nonlinear programming problem, and the maximization down range trajectory was obtained based on adaptive mesh refinement pseudospectral methods. However, sometimes it is difficult to find interior points without position constraints. A novel optimization strategy based on dynamic programming theory was proposed to search the free interior points more accurately and quickly, which resulted in almost the same optimized trajectory while producing a small mesh. The results of numerical examples showed that the boost-glide vehicle trajectory optimization problem is solved using the adaptive mesh refinement pseudospectral methods.