  This work considers the optimal covariance steering problem for systems
subject to both additive noise and uncertain parameters which may enter
multiplicatively with the state and the control. The unknown parameters are
modeled as a constant random variable sampled from a distribution with known
moments. The optimal covariance steering problem is formulated using a
moment-based representation of the system dynamics, which includes dependence
between the unknown parameters and future states, and is solved using
sequential convex programming. The proposed approach is demonstrated
numerically using a holonomic spacecraft system and an autonomous vehicle
control application.
