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Prescribed-time distributed formation control for a class of nonlinear multi-agent systems subject to internal uncertainties and external disturbances

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Abstract

This paper proposes a prescribed-time formation control scheme with low complexity and performance guarantees for second-order nonlinear multi-agent systems with a directed graph. A continuous finite-time control based on the barrier Lyapunov function and a novel performance function is proposed. Within our scheme, the unmodeled dynamics and external disturbances of the system are handled by the combination of a function approximator and a disturbance observer based on neural network and sliding mode control, respectively. Different from many existing finite-time control results, the proposed scheme can set the settling time of the closed-loop system in advance, and there is no discontinuous control term and chattering phenomenon. Finally, it is proved that all signals in the closed-loop system are uniformly ultimately bounded. At the same time, a set of numerical comparisons intuitively illustrate the effectiveness and superiority of the proposed controller.

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Data availability

The datasets during the current study are not publicly available but are available from the corresponding author on reasonable request.

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Funding

This work was supported by the Tianjin Natural Science Foundation of China (No. 20JCYBJC01060), the National Natural Science Foundation of China (No. 62103203, 61973175), the Fundamental Research Funds for the Central Universities under Grant (No. 63201196).

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Authors and Affiliations

  1. College of Artificial Intelligence, Nankai University, Tianjin, 300350, China

    Yunbiao Jiang, Zhongxin Liu & Zengqiang Chen

  2. Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin, 300350, China

    Zhongxin Liu & Zengqiang Chen

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  1. Yunbiao Jiang
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  2. Zhongxin Liu
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Correspondence to Zhongxin Liu.

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Jiang, Y., Liu, Z. & Chen, Z. Prescribed-time distributed formation control for a class of nonlinear multi-agent systems subject to internal uncertainties and external disturbances. Nonlinear Dyn 111, 1643–1655 (2023). https://doi.org/10.1007/s11071-022-07909-2

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  • DOI: https://doi.org/10.1007/s11071-022-07909-2

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