  The problem of optimizing affine feedback laws that explicitly steer the mean
and covariance of an uncertain system state in the presence of a Gaussian
random field is considered. Spatially-dependent disturbances are successively
approximated with respect to a nominal trajectory by a sequence of jointly
Gaussian random vectors. Sequential updates to the nominal control inputs are
computed via convex optimization that includes the effect of affine state
feedback, the perturbing effects of spatial disturbances, and chance
constraints on the closed-loop state and control. The developed method is
applied to solve for an affine feedback law to minimize the 99th percentile of
$\Delta v$ required to complete an aerocapture mission around a planet with a
randomly disturbed atmosphere.
