no code implementations • 1 Sep 2021 • Tongjia Zheng, Qing Han, Hai Lin
In this work, we present a backstepping design algorithm that extends density control to heterogeneous and higher-order stochastic systems in strict-feedback forms.
no code implementations • 9 Jun 2021 • Tongjia Zheng, Qing Han, Hai Lin
This work studies how to estimate the mean-field density of large-scale systems in a distributed manner.
no code implementations • 2 Jun 2021 • Tongjia Zheng, Qing Han, Hai Lin
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.
no code implementations • 2 Jun 2021 • Tongjia Zheng, Hai Lin
Recent years have seen an increased interest in using mean-field density based modelling and control strategy for deploying robotic swarms.
no code implementations • 10 Sep 2020 • Tongjia Zheng, Hai Lin
In this work, we further study how to decentralize the density filter such that each agent can estimate the global density only based on its local observation and communication with neighbors.
no code implementations • 20 Jun 2020 • Tongjia Zheng, Qing Han, Hai Lin
With the rapid development of AI and robotics, transporting a large swarm of networked robots has foreseeable applications in the near future.