Orbital Averaging and Control Parametrization for Many-Revolution Low-Thrust Transfer Trajectory Optimization

Abstract

With the increase in the use of Solar Electric Propulsion for Earth-centered applications, comes an additional challenge in the field of low-thrust spacecraft trajectory optimization. This MSc project implements a hybrid optimization approach, using control parametrization to reduce the number of optimization parameters and orbital averaging to decreasing computational speeds. Several improvements are made in comparison to previous works, using co-state scaling, and self-adaptive differential evolution to improve convergence. Additionally, variable-step integration of the orbital state averages improved propagation speed. The implemented optimization tool was applied to the GTO to GEO transfer problem, demonstrating improved convergence compared to the previous authors, with the exception of eclipse conditions, requiring further verification. Additionally, a transfer with significant plane changes to a space debris object in LEO was investigated, including oblateness and aerodynamic perturbations. Without any manual tuning, nor a-priori estimates, the optimization tool is able to find time-optimal trajectories. The tool shows remarkable flexibility, allowing additional perturbations or operational constraints, providing a powerful additional asset to Tudat for the preliminary design of low-thrust transfer trajectories.