Distribution Steering for Discrete-Time Uncertain Ensemble Systems

TL;DR

This paper develops a method to steer the probability distribution of discrete-time uncertain ensemble systems, using moments to handle non-Gaussian distributions and providing a control law for distribution shaping.

Contribution

It introduces a novel distribution steering approach for discrete-time ensemble systems with arbitrary distributions, extending beyond Gaussian assumptions.

Findings

Method effectively handles non-Gaussian distributions.

Finite-dimensional control law derived for moment system.

Numerical example validates theoretical approach.

Abstract

Ensemble systems appear frequently in many engineering applications and, as a result, they have become an important research topic in control theory. These systems are best characterized by the evolution of their underlying state distribution. Despite the work to date, few results exist dealing with the problem of directly modifying (i.e., ``steering'') the distribution of an ensemble system. In addition, in most existing results, the distribution of the states of an ensemble of discrete-time systems is assumed to be Gaussian. However, in case the system parameters are uncertain, it is not always realistic to assume that the distribution of the system follows a Gaussian distribution, thus complicating the solution of the overall problem. In this paper, we address the general distribution steering problem for first-order discrete-time ensemble systems, where the distributions of the system parameters and the states are arbitrary with finite first few moments. Linear system dynamics are considered using the method of power moments to transform the original infinite-dimensional problem into a finite-dimensional one. We also propose a control law for the ensuing moment system, which allows us to obtain the power moments of the desired control inputs. Finally, we solve the inverse problem to obtain the feasible control inputs from their corresponding power moments. We provide a numerical example to validate our theoretical developments.