Density Steering by Power Moments

TL;DR

This paper introduces a method for steering probability densities in stochastic systems using power moments, converting an infinite-dimensional control problem into a finite-dimensional one with an empirical control scheme.

Contribution

It generalizes stochastic control by employing power moments to handle non-Gaussian distributions and provides an analytic control law via convex optimization.

Findings

Control law ensures positive moment sequences

Finite-dimensional control scheme validated by numerical examples

Existence and uniqueness of solutions established

Abstract

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.