This article addresses the distributed leader escort control problem for multiple autonomous surface vessels (multi-ASVs) by adopting a signed graph-based modeling approach to represent interaction relationships among the ASVs. Within this framework, the ASVs are classified into two groups, with the control objective of forming time-varying formations on either side of the dynamic leader while maintaining consistent distances. One challenge in addressing this issue is that only a subset of the following ASVs has access to the escort information and the motion data of the leader. Focusing on scenarios with only external disturbances, we introduce a predefined-time escort control scheme that confines error systems within a designated manifold using two auxiliary time-varying functions. It is proven that the predefined-time leader escort can be achieved under the present control scheme with appropriate gain parameters. To address the leader escort control problem in the presence of internal model uncertainties and external disturbances, we develop a fully distributed robust adaptive leader escort controller that guarantees the asymptotic convergence of escort errors. Specifically, neural networks and nonsmooth feedback are employed to approximate model uncertainties and to compensate for unknown bounded disturbances, respectively. Notably, the control gains are adaptively adjusted without reliance on any global information. The efficacy of the proposed escort controllers is verified through comprehensive simulation and experimental studies.
机构:
Dalian Maritime Univ, Sch Marine Elect Engn, Dalian 116026, Peoples R ChinaDalian Maritime Univ, Sch Marine Elect Engn, Dalian 116026, Peoples R China
Lv, Guanghao
Peng, Zhouhua
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机构:
Dalian Maritime Univ, Sch Marine Elect Engn, Dalian 116026, Peoples R ChinaDalian Maritime Univ, Sch Marine Elect Engn, Dalian 116026, Peoples R China
Peng, Zhouhua
Liu, Lu
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Dalian Maritime Univ, Sch Marine Elect Engn, Dalian 116026, Peoples R ChinaDalian Maritime Univ, Sch Marine Elect Engn, Dalian 116026, Peoples R China
Liu, Lu
Wang, Jun
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机构:
City Univ Hong Kong, Dept Comp Sci, Hong Kong, Peoples R China
City Univ Hong Kong, Sch Data Sci, Hong Kong, Peoples R ChinaDalian Maritime Univ, Sch Marine Elect Engn, Dalian 116026, Peoples R China
Wang, Jun
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS,
2023,
53
(01):
: 563
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575