Learning‐based hierarchical control for fault‐tolerant cooperation of networked marine surface vehicles

In this article, an efficient hierarchical control framework is proposed to address the cooperation problems (e.g., consensus tracking, formation tracking, and time‐varying formation tracking) for the networked marine surface vehicles in the presence of external disturbances, actuator faults and fai...

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Veröffentlicht in:International journal of robust and nonlinear control Jg. 32; H. 9; S. 5717 - 5740
Hauptverfasser: Liang, Chang‐Duo, Ge, Ming‐Feng, Wang, Leimin, Liu, Zhi‐Wei, Li, Bo
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Bognor Regis Wiley Subscription Services, Inc 01.06.2022
Schlagworte:
actuator faults and failures
Actuators
Algorithms
Control algorithms
Control systems design
Controllers
Cooperation
fault‐tolerant cooperation
Iterative methods
learning‐based hierarchical control algorithm
Machine learning
networked marine surface vehicles
Surface vehicles
Tracking
ISSN:1049-8923, 1099-1239
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Zusammenfassung:In this article, an efficient hierarchical control framework is proposed to address the cooperation problems (e.g., consensus tracking, formation tracking, and time‐varying formation tracking) for the networked marine surface vehicles in the presence of external disturbances, actuator faults and failures. Based on this framework, several learning‐based hierarchical control algorithms are developed, involving an iterative learning‐based estimator and a local observer‐based finite‐time controller. The estimator is designed to achieve sufficiently precise estimation of the leader states through enough iterations, while the observer‐based finite‐time controller is used to observe and compensate the dynamic uncertainties as well as stabilize the error states in a finite time. By using the theories of Hurwitz, Schur, and Lyapunov stability, the sufficient conditions for guaranteeing the convergence of these learning‐based hierarchical control algorithms are derived. Finally, numerical simulations are performed on the Cyber‐Ships II to verify the effectiveness of the presented algorithms.
Bibliographie:Funding information
This research is supported by the National Natural Science Foundation of China (Grants: 62073301, 61973133, 62076229), the Natural Science Foundation of Hubei Province of China (Grant 2021CFB516), the National Key Technology R&D Program of China (Grants: 2020YFB1709301, 2020YFB1709304), the Innovative Development Project for Supporting Enterprise Technology of Hubei Province of China (Grant: 2021BAB094), the National Key Scientific Research Instruments and Equipment Development Projects (Grant: 41827808), and in part by the Fundamental Research Funds for National University, China University of Geosciences (Wuhan).
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ISSN:1049-8923
1099-1239
DOI:10.1002/rnc.6107