  This work examines the optimal covariance steering problem for systems
subject to unknown parameters that enter multiplicatively with the state and
control, in addition to additive disturbances. In contrast to existing works,
the unknown parameters are modeled as random variables and are estimated
online. This work proposes the utilization of recursive least squares
estimation for efficient parameter identification. A dual control problem is
formulated in which the effect of the planned control policy on the parameter
estimates is modeled and optimized for. The parameter estimates are then used
to modify the pre-computed control policy online in an adaptive control
fashion. Finally, the proposed approach is demonstrated in a vehicle control
example with closed-loop parameter identification.
