Generalized Cyclic Pursuit: A Model-Reference Adaptive Control Approach

Antoine Ansart, Jyh Ching Juang

研究成果: Conference contribution

1 引文 斯高帕斯(Scopus)

摘要

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.

原文English
主出版物標題2020 5th International Conference on Control and Robotics Engineering, ICCRE 2020
發行者Institute of Electrical and Electronics Engineers Inc.
頁面89-94
頁數6
ISBN(電子)9781728167916
DOIs
出版狀態Published - 2020 四月
事件5th International Conference on Control and Robotics Engineering, ICCRE 2020 - Osaka, Japan
持續時間: 2020 四月 242020 四月 26

出版系列

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

Conference

Conference5th International Conference on Control and Robotics Engineering, ICCRE 2020
國家Japan
城市Osaka
期間20-04-2420-04-26

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Mechanical Engineering
  • Control and Optimization

指紋 深入研究「Generalized Cyclic Pursuit: A Model-Reference Adaptive Control Approach」主題。共同形成了獨特的指紋。

引用此