TY - GEN
T1 - Fully Distributed Prescribed-time Consensus
T2 - 2025 IEEE International Conference on Unmanned Systems, ICUS 2025
AU - Liu, Enbo
AU - Zhou, Yuan
AU - Liu, Yongfang
AU - Zhao, Yu
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - This paper focuses on the fully distributed prescribed-time consensus (PTC) problem in multi-agent systems. First, leveraging an adaptive time-base generator (TBG) strategy, a class of fully distributed prescribed-time consensus protocols is designed. Compared with existing results, the proposed approach does not need global network parameters, thereby achieving fully distributed control. Then, to enhance robustness against unknown bounded disturbances, by incorporating a signum function within the adaptive TBG framework, a robust prescribed-time consensus algorithm is developed. Further, the proposed framework is extended to the leader setting, a prescribed-time leader-following consensus (PTLFC) algorithm is designed to ensure that all followers track a dynamic leader within a prescribed settling time. As a potential application, the approach is employed to solve a formation tracking task, and corresponding numerical simulation is provided to validate the accuracy and robustness of algorithms.
AB - This paper focuses on the fully distributed prescribed-time consensus (PTC) problem in multi-agent systems. First, leveraging an adaptive time-base generator (TBG) strategy, a class of fully distributed prescribed-time consensus protocols is designed. Compared with existing results, the proposed approach does not need global network parameters, thereby achieving fully distributed control. Then, to enhance robustness against unknown bounded disturbances, by incorporating a signum function within the adaptive TBG framework, a robust prescribed-time consensus algorithm is developed. Further, the proposed framework is extended to the leader setting, a prescribed-time leader-following consensus (PTLFC) algorithm is designed to ensure that all followers track a dynamic leader within a prescribed settling time. As a potential application, the approach is employed to solve a formation tracking task, and corresponding numerical simulation is provided to validate the accuracy and robustness of algorithms.
KW - adaptive time-based generator strategy
KW - fully distributed consensus
KW - prescribed-time control
UR - https://www.scopus.com/pages/publications/105031894885
U2 - 10.1109/ICUS66297.2025.11295240
DO - 10.1109/ICUS66297.2025.11295240
M3 - 会议稿件
AN - SCOPUS:105031894885
T3 - Proceedings of 2025 IEEE International Conference on Unmanned Systems, ICUS 2025
SP - 661
EP - 666
BT - Proceedings of 2025 IEEE International Conference on Unmanned Systems, ICUS 2025
A2 - Song, Rong
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 18 September 2025 through 19 September 2025
ER -