Particle Swarm Optimization Based LQ-Servo Controller for Congestion Avoidance

Abstract

The network congestion is an essential problem that leads to packets losing and performance degradation. Thus, preventing congestion in the network is very important to enhance and improve the quality of service. Active queue management (AQM) is the solution to control congestion in TCP network middle nodes to improve theire performance. We design a linear quadratic (LQ)-servo controller as an AQM applied to TCP network to control congestion and attempt to achieve high quality of service under dynamic network environments. The LQ-servo controller is proposed to provide queue length stabilization with a small delay and faster settling time. The designed controller parameters are tuned by using the particle swarm optimization (PSO) method. The PSO algorithm was fundamentally applied to find the optimal controller parameters Q and R, such that a good output response could be obtained. The PI controller is examined for comparison reasons. The MATLAB simulation result shows that the controller is more effective than the PI in reaching zero steadystate error with better congestion avoidance under the dynamic network environment. Moreover, the proposed controller achieves a smaller delay and faster settling time