Neural Network-Based Nonlinear Multiagent Systems Tracking Control with Dynamic Connectivity Preservation
This paper delves into a specific category of multiagent systems marked by nonlinear uncertainties. It tackles the distributed adaptive consensus tracking issue while emphasizing dynamic connectivity preservation. Considering the presence of uncertain nonlinear terms in the system, neural networks a...
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Published in | 2024 6th International Conference on Electronic Engineering and Informatics (EEI) pp. 723 - 728 |
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Main Authors | , , |
Format | Conference Proceeding |
Language | English |
Published |
IEEE
28.06.2024
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Subjects | |
Online Access | Get full text |
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Summary: | This paper delves into a specific category of multiagent systems marked by nonlinear uncertainties. It tackles the distributed adaptive consensus tracking issue while emphasizing dynamic connectivity preservation. Considering the presence of uncertain nonlinear terms in the system, neural networks are utilized approximate these nonlinear terms. Taking into account the dynamic transformation range of communication among agents, an adaptive neural network controller is designed using an error transformation method to ensure the initial dynamic connectivity preservation patterns. Furthermore, the proposed control method is demonstrated to maintain boundedness for all signals through Lyapunov theory. Numerical simulations provide evidence of the effectiveness of the proposed control method. |
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DOI: | 10.1109/EEI63073.2024.10696001 |