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Event-triggered fixed-time adaptive neural formation control for underactuated ASVs with connectivity constraints and prescribed performance

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Abstract

In this paper, an event-triggered fixed-time adaptive neural network formation control method is proposed for underactuated multiple autonomous surface vessels with model uncertainties and unknown external disturbances under communication distance constraints. First, a time-varying barrier Lyapunov function is developed to obtain prescribed performances, such as formation error constraints, and avoid collisions in the communication range with distance limitations. Second, combining backstepping technology with neural networks, a fixed-time adaptive minimum learning parameter (MLP) is proposed to improve robustness against external disturbances and model uncertainties, and an adaptive law is designed to compensate for the approximation error of MLP. Third, a relative threshold-based event-triggered strategy is developed to greatly save communication resources without degrading control performance. Subsequently, Theorem analysis shows that all signals in the closed-loop system are bounded and practical fixed-time stable. Finally, the effectiveness of the proposed method is demonstrated by numerical simulations.

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The data that support the findings of this study are available on request from the corresponding author.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China [Grant number 52171346], the Key Project of DEGP [Grant number 2021ZDZX1041], the Shenzhen Science and Technology Program [Grant number JCYJ20220530162014033], the 2019 “Chong First-class" Provincial Financial Special Funds Construction Project [Grant number 231419019], the Science and Technology Planning Project of Zhanjiang City [2021E05012, 2021A05023], and the Zhanjiang innovation and entrepreneurship team lead "pilot plan" project [Grant number 2020LHJH003].

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Authors and Affiliations

  1. Shenzhen Institute of Guangdong Ocean University, Shenzhen, 518120, China

    Haitao Liu, Jianfei Lin, Xuehong Tian & Qingqun Mai

  2. School of Mechanical Engineering, Guangdong Ocean University, Zhanjiang, 524088, China

    Haitao Liu, Jianfei Lin, Ronghui Li, Xuehong Tian & Qingqun Mai

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  1. Haitao Liu
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Correspondence to Haitao Liu.

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Liu, H., Lin, J., Li, R. et al. Event-triggered fixed-time adaptive neural formation control for underactuated ASVs with connectivity constraints and prescribed performance. Neural Comput & Applic 35, 13485–13501 (2023). https://doi.org/10.1007/s00521-023-08417-z

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