期刊
IEEE TRANSACTIONS ON CYBERNETICS
卷 51, 期 9, 页码 4611-4622出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2020.3009992
关键词
Observers; Task analysis; Sea surface; Adaptation models; Velocity measurement; Urban areas; Cybernetics; Autonomous surface vehicles (ASVs); data-driven adaptive extended state observer (ESO); disturbances; flocking; unknown control gains
类别
资金
- Research Grants Council of the Hong Kong Special Administrative Region of China [11208517, 11202318]
- International Partnership Program of Chinese Academy of Sciences [GJHZ1849]
- National Natural Science Foundation of China [61673081, 51979020, 51909021, 61673330]
- Science and Technology Fund for Distinguished Young Scholars of Dalian [2018RJ08]
- High-Level Talent Development Program in Transportation Department [2018-030]
- National Key Research and Development Program of China [2016YFC0301500]
- Fundamental Research Funds for the Central Universities [3132019319, 3132020101]
- China Postdoctoral Science Foundation [2019M650086]
- Stable Supporting Fund of Science and Technology on Underwater Vehicle Technology [JCKYS2019604SXJQR-01]
This article proposes a data-driven adaptive ESO-based output-feedback ASV flocking control method guided by a parameterized path, addressing the control problem of a swarm of autonomous surface vehicles. The leading ASV and following ASVs estimate velocities through a cooperative estimation network to achieve flocking control.
This article addresses an output-feedback flocking control problem for a swarm of autonomous surface vehicles (ASVs) to follow a leading ASV guided via a parameterized path. The leading and following ASVs are subject to completely unknown model parameters, external disturbances, and unmeasured velocities. A data-driven adaptive anti-disturbance control method is proposed for establishing a flocking behavior without any prior knowledge of model parameters. Specifically, a data-driven adaptive extended state observer (ESO) is proposed such that unknown input gains, unmeasured velocities, and total disturbance are simultaneously estimated. For the leading ASV, an output-feedback path-following control law is developed to follow a predefined parameterized path. For following ASVs, an output-feedback flocking control law is developed based on an artificial potential function for collision avoidance and connectivity preservation, in addition to a distributed ESO for estimating the velocity of the leading ASV through a cooperative estimation network. The simulation results are discussed to substantiate the efficacy of the proposed path-guided output-feedback ASV flocking control based on data-driven adaptive ESOs without measured velocity information.
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