Adaptive Finite-Time Funnel Congestion Control of TCP/AWM Network Systems

doi:10.12068/j.issn.1005-3026.2022.10.001

Abstract

Abstract: The congestion control problem for TCP/AWM network systems with external disturbances was studied. Firstly， in order to ensure the queue tracking error with preassigned transient and steady-state performance， a funnel error transformation was introduced to limit queue tracking error. Secondly， RBF neural network was used to deal with the nonlinear terms in the network system. An active window management algorithm was proposed by combining funnel control， finite-time control， adaptive backstepping technique and RBF neural network. The proposed control algorithm ensures that all signals of the closed-loop system are semi-globally practically finite-time bounded， and the queue tracking error converges to the prescribed funnel boundary. Finally， the proposed method is compared with the existing two similar algorithms， and the simulation results show that the designed controller makes the system have a faster convergence speed and a smaller overshoot， which further verifies the feasibility and superiority of the proposed method.

Key words: TCP/AWM network; congestion control; funnel boundary; finite-time control; RBF neural network

CLC Number:

TP393

JING Yuan-wei， XIE Hai-xiu， BAI Yun. Adaptive Finite-Time Funnel Congestion Control of TCP/AWM Network Systems[J]. Journal of Northeastern University(Natural Science), 2022, 43(10): 1369-1375.

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