Generalized Cyclic Pursuit: A Model-Reference Adaptive Control Approach

Antoine Ansart; Jyh Ching Juang

doi:10.1109/ICCRE49379.2020.9096453

Generalized Cyclic Pursuit: A Model-Reference Adaptive Control Approach

Antoine Ansart, Jyh Ching Juang

Department of Electrical Engineering

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

6 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 language	English
Title of host publication	2020 5th International Conference on Control and Robotics Engineering, ICCRE 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	89-94
Number of pages	6
ISBN (Electronic)	9781728167916
DOIs	https://doi.org/10.1109/ICCRE49379.2020.9096453
Publication status	Published - 2020 Apr
Event	5th International Conference on Control and Robotics Engineering, ICCRE 2020 - Osaka, Japan Duration: 2020 Apr 24 → 2020 Apr 26

Publication series

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

Conference

Conference	5th International Conference on Control and Robotics Engineering, ICCRE 2020
Country/Territory	Japan
City	Osaka
Period	20-04-24 → 20-04-26

All Science Journal Classification (ASJC) codes

Artificial Intelligence
Mechanical Engineering
Control and Optimization

Access to Document

10.1109/ICCRE49379.2020.9096453

Cite this

Ansart, A., & Juang, J. C. (2020). Generalized Cyclic Pursuit: A Model-Reference Adaptive Control Approach. In 2020 5th International Conference on Control and Robotics Engineering, ICCRE 2020 (pp. 89-94). Article 9096453 (2020 5th International Conference on Control and Robotics Engineering, ICCRE 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCRE49379.2020.9096453

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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.",

author = "Antoine Ansart and Juang, {Jyh Ching}",

note = "Funding Information: ACKNOWLEDGEMENTS The research is supported by the Ministry of Science and Technology (MOST), Taiwan, under grant MOST 107-2218-E-006-059. Funding Information: The research is supported by the Ministry of Science and Technology (MOST), Taiwan, under grant MOST 107-2218-E-006-059. Publisher Copyright: {\textcopyright} 2020 IEEE.; 5th International Conference on Control and Robotics Engineering, ICCRE 2020 ; Conference date: 24-04-2020 Through 26-04-2020",

year = "2020",

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doi = "10.1109/ICCRE49379.2020.9096453",

language = "English",

series = "2020 5th International Conference on Control and Robotics Engineering, ICCRE 2020",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Ansart, A & Juang, JC 2020, Generalized Cyclic Pursuit: A Model-Reference Adaptive Control Approach. in 2020 5th International Conference on Control and Robotics Engineering, ICCRE 2020., 9096453, 2020 5th International Conference on Control and Robotics Engineering, ICCRE 2020, Institute of Electrical and Electronics Engineers Inc., pp. 89-94, 5th International Conference on Control and Robotics Engineering, ICCRE 2020, Osaka, Japan, 20-04-24. https://doi.org/10.1109/ICCRE49379.2020.9096453

Generalized Cyclic Pursuit: A Model-Reference Adaptive Control Approach. / Ansart, Antoine; Juang, Jyh Ching.
2020 5th International Conference on Control and Robotics Engineering, ICCRE 2020. Institute of Electrical and Electronics Engineers Inc., 2020. p. 89-94 9096453 (2020 5th International Conference on Control and Robotics Engineering, ICCRE 2020).

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

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N1 - Funding Information: ACKNOWLEDGEMENTS The research is supported by the Ministry of Science and Technology (MOST), Taiwan, under grant MOST 107-2218-E-006-059. Funding Information: The research is supported by the Ministry of Science and Technology (MOST), Taiwan, under grant MOST 107-2218-E-006-059. Publisher Copyright: © 2020 IEEE.

PY - 2020/4

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N2 - 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.

AB - 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.

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Ansart A, Juang JC. Generalized Cyclic Pursuit: A Model-Reference Adaptive Control Approach. In 2020 5th International Conference on Control and Robotics Engineering, ICCRE 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 89-94. 9096453. (2020 5th International Conference on Control and Robotics Engineering, ICCRE 2020). doi: 10.1109/ICCRE49379.2020.9096453

Generalized Cyclic Pursuit: A Model-Reference Adaptive Control Approach

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