Generalized Cyclic Pursuit: A Model-Reference Adaptive Control Approach

Antoine Ansart, Jyh Ching Juang

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

2 Citations (Scopus)

Abstract

The paper proposes a method about sustaining the motion of a group of autonomous agents under the Generalized Cyclic Pursuit (GCP) laws. Under GCP, formation patterns can be formed by assigning eigenvalues of the system to be marginally stable. Such a control, however, is sensitive to parameter variation. In the paper, Model Reference Adaptive Control (MRAC) technique is employed to sustain the motion of agents and thus maintain the desired patterns in the presence of uncertainties. Simulation results are provided to verify the proposed approach.

Original languageEnglish
Title of host publication2020 5th International Conference on Control and Robotics Engineering, ICCRE 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages89-94
Number of pages6
ISBN (Electronic)9781728167916
DOIs
Publication statusPublished - 2020 Apr
Event5th International Conference on Control and Robotics Engineering, ICCRE 2020 - Osaka, Japan
Duration: 2020 Apr 242020 Apr 26

Publication series

Name2020 5th International Conference on Control and Robotics Engineering, ICCRE 2020

Conference

Conference5th International Conference on Control and Robotics Engineering, ICCRE 2020
Country/TerritoryJapan
CityOsaka
Period20-04-2420-04-26

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Mechanical Engineering
  • Control and Optimization

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