  This paper presents an estimation and control framework that enables the
targeted reentry of a drag-modulated spacecraft in the presence of atmospheric
density uncertainty. In particular, an extended Kalman filter (EKF) is used to
estimate the in-flight density errors relative to the atmospheric density used
to generate the nominal guidance trajectory. This information is leveraged
within a model predictive control (MPC) strategy to improve tracking
performance, reduce control effort, and increase robustness to actuator
saturation compared to the state-of-the-art approach. The estimation and
control framework is tested in a Monte Carlo simulation campaign with
historical space weather data. These simulation efforts demonstrate that the
proposed framework is able to stay within 100 km of the guidance trajectory at
all points in time for 98.4% of cases. The remaining 1.6% of cases were pushed
away from the guidance by large density errors, many due to significant solar
storms and flares, that could not physically be compensated for by the drag
control device. For the successful cases, the proposed framework was able to
guide the spacecraft to the desired location at the entry interface altitude
with a mean error of 12.1 km and 99.7% of cases below 100 km.
