Reconstructing trajectories from the moments of occupation measures

Moment optimization techniques have been recently proposed to solve globally various classes of optimal control problems. Since those methods return truncated moment sequences of occupation measures, this paper explores a numerical method for reconstructing optimal trajectories and controls from this data. By approximating occupation measures by atomic measures on a given grid, the problem reduces to a finite-dimensional linear program. In contrast with earlier numerical methods, this linear program is guaranteed to be feasible, no tolerance needs to be specified, and its size can be properly controlled. When combined with local optimal control solvers, this yields a powerful and flexible numerical approach for tackling difficult control problems, as demonstrated by examples.