Congestion tracking control for uncertain TCP/AQM network based on integral backstepping

Area of Science:

• Control Systems Engineering
• Computer Networks
• Network Congestion Control

Background:

• Nonlinear Transmission Control Protocol/Active Queue Management (TCP/AQM) networks face challenges with external disturbances and modeling uncertainties.
• Existing network models require enhancements to accurately simulate dynamic queue length variations.

Purpose of the Study:

• To propose an H∞ congestion tracking control for nonlinear TCP/AQM networks.
• To introduce a novel function for desired queue length simulation, reflecting daily variations.
• To develop a new Active Queue Management (AQM) scheme for effective network congestion control.

Main Methods:

• Development of a modified TCP/AQM network model incorporating external disturbance and uncertainty.
• Definition of a novel desired queue length function to simulate diurnal queue length changes.
• Integration of H∞ theory with integral backstepping technique for the AQM scheme.

Main Results:

• The proposed AQM scheme achieves superior queue length tracking performance.
• All signals within the closed-loop system are proven to be asymptotically stable in probability.
• Simulation results demonstrate the effectiveness of the presented control algorithm.

Conclusions:

• The novel H∞ congestion tracking control method effectively manages network congestion in nonlinear TCP/AQM systems.
• The integrated approach ensures robust performance and stability under dynamic network conditions.
• The developed AQM scheme offers a promising solution for improving network performance and reliability.