A Novel Intelligent PID Controller for AQM Based on Neural Networks

Nowadays Congestion control problem of the intermediate nodes in the Internet has received extensively attention in control community. In this paper, a novel intelligent PID (Proportional-Integral-Differential) controller based on neural networks (PIDNN) for the problem of AQM is presented. Considering a previously developed nonlinear dynamic model of TCP/AQM system and the queue management mechanism of intermediate nodes, the parameters of AQM controller based on PIDNN is tuned online by using gradient-descent algorithm, and the probability of packet dropout is obtained adaptively to measure the degree of congestion in time, so that the quality of service (QoS) of network and the transient performance can be improve greatly especially when the network parameters are time-varying. Finally, the proposed algorithm is verified by using NS-2 simulator, and simulation results show that the integrated performance of this proposed controller is obviously superior to those of common PID controller especially on the queue stability and loss probability and etc. Furthermore, this AQM algorithm has simple structure and can be implemented easily.