Neural Network-Based Distributed Adaptive Pre-Assigned Finite-Time Consensus of Multiple TCP/AQM Networks

In this study, a class of finite-time consensus of multiple transmission control protocol/active queue management (TCP/AQM) networks is investigated on the basis of a design idea of multi-agent systems, and for the first time, to our knowledge, a novel congestion control concept with neural networks...

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Bibliographic Details
Published in:IEEE transactions on circuits and systems. I, Regular papers Regular papers, 2021-01, Vol.68 (1), p.387-395
Main Authors: Wang, Chunmei, Chen, Xiangyong, Cao, Jinde, Qiu, Jianlong, Liu, Yang, Luo, Yiping
Format: Article
Language:English
Subjects:
Active control
Adaptive control
Automation
Backstepping
Closed loop systems
Complex systems
Design parameters
distributed control
Feedback control
Finite-time consensus
Liapunov functions
Multi-agent systems
Multiagent systems
multiple TCP/AQM networks
neural network
Neural networks
TCP (protocol)
Time functions
Tracking errors
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Summary:In this study, a class of finite-time consensus of multiple transmission control protocol/active queue management (TCP/AQM) networks is investigated on the basis of a design idea of multi-agent systems, and for the first time, to our knowledge, a novel congestion control concept with neural networks is proposed. First, the problem statement and design goal of the consensus of multiple TCP/AQM networks is given. Then, a pre-assigned finite-time function is introduced to ensure that the tracking error approaches a pre-defined area within finite time. Furthermore, by combining a barrier Lyapunov function and backstepping technique, a neural network-based distributed adaptive finite-time control protocol for the output consensus of multiple TCP/AQM networks is presented, which can effectively generate the desired controls and ensure that the convergent time of all errors has nothing to do with the initial condition and design parameters. In addition, all signals in the closed-loop system are bounded. Finally, an example is given to further illustrate the effectiveness of the theoretical finding presented.
ISSN:1549-8328
1558-0806
DOI:10.1109/TCSI.2020.3031663