  The goal of this paper is to address finite-horizon minimum variance and
covariance steering problems for discrete-time stochastic (Gaussian) linear
systems. On the one hand, the minimum variance problem seeks for a control
policy that will steer the state mean of an uncertain system to a prescribed
quantity while minimizing the trace of its terminal state covariance (or
variance). On the other hand, the covariance steering problem seeks for a
control policy that will steer the covariance of the terminal state to a
prescribed positive definite matrix. We propose a solution approach that relies
on the stochastic version of the affine disturbance feedback control
parametrization according to which the control input at each stage can be
expressed as an affine function of the history of disturbances that have acted
upon the system. Our analysis reveals that this particular parametrization
allows one to reduce the stochastic optimal control problems considered herein
into tractable convex programs with essentially the same decision variables.
This is in contrast with other control policy parametrizations, such as the
state feedback parametrization, in which the decision variables of the convex
program do not coincide with the controller's parameters of the stochastic
optimal control problem. In addition, we propose a variation of the control
parametrization which relies on truncated histories of past disturbances. We
show that by selecting the length of the truncated sequences appropriately, we
can design suboptimal controllers which can strike the desired balance between
performance and computational cost.
