Generalized cyclic pursuit: An estimator-based model-reference adaptive control approach

Antoine Ansart, Jyh Ching Juang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

The paper proposes an estimation and control method about sustaining the motion of a group of autonomous agents under the Generalized Cyclic Pursuit (GCP) laws, where formation patterns can be formed by assigning eigenvalues of the system to be marginally stable. In the present paper, a Linear Quadratic Estimator (LQE), used to estimate the absolute position based on information exchange, is coupled with a Model Reference Adaptive Control (MRAC) to sustain the motion of agents and thus maintain the desired patterns in the presence of uncertainties and noise. Simulation results are provided to verify the proposed approach in area coverage applications.

Original languageEnglish
Title of host publication2020 28th Mediterranean Conference on Control and Automation, MED 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages598-604
Number of pages7
ISBN (Electronic)9781728157429
DOIs
Publication statusPublished - 2020 Sep
Event28th Mediterranean Conference on Control and Automation, MED 2020 - Saint-Raphael, France
Duration: 2020 Sep 152020 Sep 18

Publication series

Name2020 28th Mediterranean Conference on Control and Automation, MED 2020

Conference

Conference28th Mediterranean Conference on Control and Automation, MED 2020
Country/TerritoryFrance
CitySaint-Raphael
Period20-09-1520-09-18

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Control and Optimization
  • Modelling and Simulation

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