Pseudospectral Model Predictive Convex Programming for Mars Entry Trajectory Planning
摘要
This chapter develops two approaches to the Mars entry trajectory optimization problem. The pseudospectral model predictive convex programming (PMPCP) method allows for the establishment of a pseudospectral sensitivity relation, which reduces the computational load. And the mapped Chebyshev-Gauss-Lobatto pseudospectral model predictive convex programming (MCGL-MPCP) method introduces Kosloff-Tal-Eaer conformal mapping and barycentric Lagrange interpolation, ensuring both computational efficiency and numerical accuracy.