Feedback Interconnected Mean-Field Density Estimation and Control

TL;DR

This paper investigates the interaction between density estimation and control in swarm robotic systems modeled by mean-field PDEs, proposing new feedback laws and filtering algorithms to ensure stability and convergence.

Contribution

It introduces novel density control laws using mean-field density and gradient feedback, and designs filtering algorithms with proven convergence and stability in interconnected systems.

Findings

Proposed density feedback control laws are globally ISS.

Designed filtering algorithms for density and gradient estimation.

Validated stability and convergence through agent-based simulations.

Abstract

Swarm robotic systems have foreseeable applications in the near future. Recently, there has been an increasing amount of literature that employs mean-field partial differential equations (PDEs) to model the time-evolution of the probability density of swarm robotic systems and uses density feedback to design stabilizing control laws that act on individuals such that their density converges to a target profile. However, it remains largely unexplored considering problems of how to estimate the mean-field density, how the density estimation algorithms affect the control performance, and whether the estimation performance in turn depends on the control algorithms. In this work, we focus on studying the interplay of these algorithms. Specifically, we propose new density control laws which use the mean-field density and its gradient as feedback, and prove that they are globally input-to-state stable (ISS) with respect to estimation errors. Then, we design filtering algorithms to estimate the density and its gradient separately, and prove that these estimates are convergent assuming the control laws are known. Finally, we show that the feedback interconnection of these estimation and control algorithms is still globally ISS, which is attributed to the bilinearity of the PDE system. An agent-based simulation is included to verify the stability of these algorithms and their feedback interconnection.