A discrete-time flocking algorithm for agents with sampled-data double-integrator dynamics

Brandon J. Wellman; Jesse B. Hoagg

doi:10.23919/ACC.2017.7963137

A discrete-time flocking algorithm for agents with sampled-data double-integrator dynamics

Brandon J. Wellman, Jesse B. Hoagg

Mechanical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Scopus citations

Abstract

We present a multi-agent control method that addresses flocking in discrete time. The method is decentralized, that is, each agent's controller relies on local sensing to determine the relative positions and velocities of nearby agents. Each agent has the discrete-time double-integrator dynamics obtained by sampling the continuous-time double integrator and applying a zero-order hold on the control input. We demonstrate with analysis and simulations that agents using the discrete-time flocking method converge to a set of flocking formations.

Original language	English
Title of host publication	2017 American Control Conference, ACC 2017
Pages	1334-1339
Number of pages	6
ISBN (Electronic)	9781509059928
DOIs	https://doi.org/10.23919/ACC.2017.7963137
State	Published - Jun 29 2017
Event	2017 American Control Conference, ACC 2017 - Seattle, United States Duration: May 24 2017 → May 26 2017

Publication series

Name	Proceedings of the American Control Conference
ISSN (Print)	0743-1619

Conference

Conference	2017 American Control Conference, ACC 2017
Country/Territory	United States
City	Seattle
Period	5/24/17 → 5/26/17

Bibliographical note

Funding Information:
This work is supported in part by the National Science Foundation (OIA-1539070) and the National Aeronautics and Space Administration (NNX10AL96) through the NASA Kentucky Space Grant.

Publisher Copyright:
© 2017 American Automatic Control Council (AACC).

ASJC Scopus subject areas

Electrical and Electronic Engineering

Access to Document

10.23919/ACC.2017.7963137

Cite this

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abstract = "We present a multi-agent control method that addresses flocking in discrete time. The method is decentralized, that is, each agent's controller relies on local sensing to determine the relative positions and velocities of nearby agents. Each agent has the discrete-time double-integrator dynamics obtained by sampling the continuous-time double integrator and applying a zero-order hold on the control input. We demonstrate with analysis and simulations that agents using the discrete-time flocking method converge to a set of flocking formations.",

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N2 - We present a multi-agent control method that addresses flocking in discrete time. The method is decentralized, that is, each agent's controller relies on local sensing to determine the relative positions and velocities of nearby agents. Each agent has the discrete-time double-integrator dynamics obtained by sampling the continuous-time double integrator and applying a zero-order hold on the control input. We demonstrate with analysis and simulations that agents using the discrete-time flocking method converge to a set of flocking formations.

AB - We present a multi-agent control method that addresses flocking in discrete time. The method is decentralized, that is, each agent's controller relies on local sensing to determine the relative positions and velocities of nearby agents. Each agent has the discrete-time double-integrator dynamics obtained by sampling the continuous-time double integrator and applying a zero-order hold on the control input. We demonstrate with analysis and simulations that agents using the discrete-time flocking method converge to a set of flocking formations.

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M3 - Conference contribution

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Y2 - 24 May 2017 through 26 May 2017

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A discrete-time flocking algorithm for agents with sampled-data double-integrator dynamics

Abstract

Publication series

Conference

Bibliographical note

ASJC Scopus subject areas

Access to Document

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