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Neural Network-Based Adaptive Containment Control Algorithms Design for Nonlinear Multiagent Systems with Switching Topologies

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Abstract

This paper discusses the containment control problem of nonlinear multiagent systems with unknown disturbance and switching topology. The novel model of nonlinear multiagent systems is given and the dynamic functions of this model are considered to be continuous. Utilizing the approximation capability of the neural networks to estimate the unknown external disturbance and unknown nonlinear functions in the dynamic functions of nonlinear multiagent systems, which designs disturbance observer and containment controller. The containment control problem can be addressed with the proposed algorithm, applying the Lyapunov functions, graph theory, and inequality techniques. The nonlinear multiagent systems are cooperatively semi-globally uniformly ultimately bounded and at the end of this paper, a simulation example is given to illustrate the effectiveness of the approach proposed.

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The data applied in this paper has been contained in the simulation parts.

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Funding

This work is supported by the Natural Science Foundation Project of Chongqing CSTC (Grant No. cstc2019jcyj-msxm2319).

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Authors and Affiliations

  1. Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University, Chongqing, 400044, People’s Republic of China

    Jie Huang, Xin Wang & Rui Xu

Authors
  1. Jie Huang
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  2. Xin Wang
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  3. Rui Xu
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Correspondence to Xin Wang.

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Huang, J., Wang, X. & Xu, R. Neural Network-Based Adaptive Containment Control Algorithms Design for Nonlinear Multiagent Systems with Switching Topologies. Cogn Comput 15, 90–102 (2023). https://doi.org/10.1007/s12559-022-10082-8

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  • DOI: https://doi.org/10.1007/s12559-022-10082-8

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