  This paper considers the problem of steering an arbitrary initial probability
density function to an arbitrary terminal one, where the system dynamics is
governed by a first-order linear stochastic difference equation. It is a
generalization of the conventional stochastic control problem where the
uncertainty of the system state is usually characterized by a Gaussian
distribution. We propose to use the power moments to turn the
infinite-dimensional problem into a finite-dimensional one and to present an
empirical control scheme. By the designed control law, the moment sequence of
the controls at each time step is positive, which ensures the existence of the
control for the moment system. We then realize the control at each time step as
a function in analytic form by a convex optimization scheme, for which the
existence and uniqueness of the solution have been proved in our previous
paper. Two numerical examples are given to validate our proposed algorithm.
