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Cognitive Computation
Erschienen in:

07.12.2022

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

verfasst von: Jie Huang, Xin Wang, Rui Xu

Erschienen in: Cognitive Computation | Ausgabe 1/2023

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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.
Vorheriger Artikel A Multilayer Network-Based Approach to Represent, Explore and Handle Convolutional Neural Networks
Nächster Artikel A Developmental Approach for Training Deep Belief Networks

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Metadaten
Titel
Neural Network-Based Adaptive Containment Control Algorithms Design for Nonlinear Multiagent Systems with Switching Topologies
verfasst von
Jie Huang
Xin Wang
Rui Xu
Publikationsdatum
07.12.2022
Verlag
Springer US
Erschienen in
Cognitive Computation / Ausgabe 1/2023
Print ISSN: 1866-9956
Elektronische ISSN: 1866-9964
DOI
https://doi.org/10.1007/s12559-022-10082-8

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