On the Performance of Discretization and Trust-Region Methods for On-Board Convex Low-Thrust Trajectory Optimization

Different discretization and trust-region methods are assessed and compared for the lowthrust fuel-optimal trajectory optimization problem using successive convex programming. In particular, the differential and integral formulations of the adaptive pseudospectral Legendre– Gauss–Radau method, an arbitrary-order Legendre–Gauss–Lobatto technique based on Hermite interpolation, and a first-order-hold discretization are considered. The number of nodes and segments is varied, and the suitability for onboard guidance is assessed. Moreover, two hard trust-region methods and a soft trust-region strategy are compared. A perturbed cubic interpolation and the propagation of the nonlinear dynamics with tangential thrust are used to generate initial guesses of varying quality and to evaluate the performance. Interplanetary transfers to a near-Earth asteroid, Venus, and asteroid Dionysus are chosen to assess the overall performance.