Group Steering: Approaches Based on Power Moments

TL;DR

This paper introduces a novel control approach for steering large groups of agents using power moments, ensuring convergence to desired distributions through convex optimization and empirical control schemes.

Contribution

It proposes a moment-based control framework for distribution steering of large agent groups, with proven convergence and a new analytic control realization method.

Findings

Terminal density converges to the desired distribution.

Control law ensures positive moment sequences.

Simulation validates effectiveness of the proposed algorithms.

Abstract

This paper considers the problem of steering a vast group of agents of which the dynamics are governed by a discrete-time first-order linear system. The group of agents are characterized as a probability density function and an occupation measure respectively in the paper and two corresponding treatments are given. We propose to use the power moments to characterize the density function/occupation measure of the agents. A moment system representation of the original system is put forward for control and an empirical control scheme corresponding to it is proposed. By the designed control law, the moment sequence of the control at each time step is positive, which ensures the existence of the control for the moment system. We then realize the control as an analytic form of function by a convex optimization scheme of which the existence and uniqueness of the solution have been proved in our previous paper. The terminal density is proved to converge to the desired terminal one, which distinguishes the proposed distribution steering scheme from other existing ones. An error analysis of the terminal density from the specified one is also provided. For the problem where the group of agents is characterized as an occupation measure, the control for each agent is determined by drawing independent and identically-distributed(i.i.d) samples from the realized analytic function. Finally we simulate both unconstrained and constrained controls of a vast group of agents, which validate our proposed algorithms.