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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Bibliographic Details
Published in2024 6th International Conference on Electronic Engineering and Informatics (EEI) pp. 723 - 728
Main Authors Xuan, Shuxing, Liang, Hongjing, Chen, Lei
Format Conference Proceeding
LanguageEnglish
Published IEEE 28.06.2024
Subjects
Closed loop systems
communication distance
dynamic connectivity preservation
Dynamic range
Informatics
Multi-agent systems
neural network
Neural networks
Nonlinear dynamical systems
Numerical models
Numerical simulation
Simulation
tracking control
Uncertainty
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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.
DOI:10.1109/EEI63073.2024.10696001