TY - GEN
T1 - Adaptive Prescribed-Time Formation Control of Surface Vehicles Using Dynamic Surface Method
AU - Wang, Ping
AU - Yu, Chengpu
AU - Lv, Maolong
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
PY - 2025
Y1 - 2025
N2 - The adaptive prescribed-time (PT) formation control is addressed in this paper for multiple unmanned surface vehicles (USVs) with parameter uncertainties and external disturbances. The novelty lies in proposing a novel dynamic surface control (DSC)-based PT forma tion algorithm. Specifically, to effectively compensate for filter errors and facilitate PT convergence, a new nonlinear filter (NLF) with an adaptive parameter estimator and a piece-wise function is first con structed within the DSC framework. Subsequently, combined with the adaptive technology, a unified PT control scheme is provided. It can ensure the achievement of expected formation pattern within a prede f ined time, while guaranteeing that formation errors converge to a user defined set. More importantly, the proposed control framework not only tackles the complexity explosion caused by conventional backstepping but also reduces the constraints on filter design parameters. Finally, the presented scheme’s validity is confirmed through simulation implementation.
AB - The adaptive prescribed-time (PT) formation control is addressed in this paper for multiple unmanned surface vehicles (USVs) with parameter uncertainties and external disturbances. The novelty lies in proposing a novel dynamic surface control (DSC)-based PT forma tion algorithm. Specifically, to effectively compensate for filter errors and facilitate PT convergence, a new nonlinear filter (NLF) with an adaptive parameter estimator and a piece-wise function is first con structed within the DSC framework. Subsequently, combined with the adaptive technology, a unified PT control scheme is provided. It can ensure the achievement of expected formation pattern within a prede f ined time, while guaranteeing that formation errors converge to a user defined set. More importantly, the proposed control framework not only tackles the complexity explosion caused by conventional backstepping but also reduces the constraints on filter design parameters. Finally, the presented scheme’s validity is confirmed through simulation implementation.
KW - Adaptive Prescribed-Time Formation Control
KW - Dynamic Surface Control
KW - Nonlinear Filter
KW - Unmanned Surface Vehicles
UR - http://www.scopus.com/inward/record.url?scp=105006416273&partnerID=8YFLogxK
U2 - 10.1007/978-981-96-2208-5_35
DO - 10.1007/978-981-96-2208-5_35
M3 - Conference contribution
AN - SCOPUS:105006416273
SN - 9789819622078
T3 - Lecture Notes in Electrical Engineering
SP - 361
EP - 371
BT - Advances in Guidance, Navigation and Control - Proceedings of 2024 International Conference on Guidance, Navigation and Control Volume 3
A2 - Yan, Liang
A2 - Duan, Haibin
A2 - Deng, Yimin
PB - Springer Science and Business Media Deutschland GmbH
T2 - International Conference on Guidance, Navigation and Control, ICGNC 2024
Y2 - 9 August 2024 through 11 August 2024
ER -