Robust Spacecraft Guidance with Control-Dependent Noise: Analysis and Application

We demonstrate a robust neighboring guidance law for multiple space mission scenarios, including asteroid Sun-terminator orbits and near-rectilinear halo orbits in the Earth-Moon system. The robust guidance law was previously derived for the optimal intermittent control of hybrid linear systems and seeks to minimize the mean squared deviation of the system’s final state from a target state in the presence of additive noise, control-dependent noise, sampled measurements, and impulsive and/or continuous control inputs. The control feedback gains are computed offline via dynamic programming such that the optimal control law is computationally efficient. In this paper, the performance of the robust guidance law is compared to that of a linear-quadratic regulator (LQR) for various mission scenarios, noise levels, and navigation/maneuver schedules. These comparisons show that the robust guidance law can reduce mean squared state errors by orders of magnitude and maintain robust performance when LQR solutions fail.