Chance-Constrained Covariance Steering in a Gaussian Random Field via Successive Convex Programming

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