A hybrid, self-adjusting search algorithm for optimal space trajectory design

The aim of the present paper is to propose a hybrid, self adjusting, search algorithm for space trajectory optimization. By taking advantage of both direct and indirect methods, the present algorithm allows the finding of the optimal solution through the introduction of some new control parameters, whose number is smaller than that of the Lagrange multipliers, and whose range is bounded. Eventually, the optimal solution is determined by means of an iterative self-adjustment of the search domain occurring at “runtime”, without any correction by an external user. This new set of parameters can be found through a reduction process of the degrees of freedom, obtained through the transversality conditions before entering the search loop. Furthermore, such a process shows that Lagrange multipliers are subject to a deep symmetry mirroring the features of the state vector. The algorithm reliability and efficiency is assessed through some test cases, and by reproducing some optimal transfer trajectories: a full three-dimensional, minimum time Mars mission, an optimal transfer to Jupiter, and finally an injection into a circular Moon orbit.