Input-Rate Based Adaptive Fuzzy Neuron PID Control for AQM

Internet routers play an important role during network congestion. All the routers have buffers at input and output ports to hold the packets at congestion. Various congestion control algorithms have been proposed to control the congestion. Recently, some proportional-integral-derivative (PID) controller based algorithms have been proposed as Active Queue Management(AQM) schemes to address performance degradations of end-to-end TCP congestion control. However, most of the proposed PM-controllers for AQM are validated for their performance and stability via intuitive explanation and simulation studies instead of theoretic analysis and performance evaluation. But there are a few drawbacks of PID-controller based AQM algorithms leading to poor performance like causing data retention dropping and oscillation when the time delay is large, which means that the existing PM-controller can not meet the Quality of Service (QoS) requirements. To overcome the drawbacks of traditional PID, we analyze and enhance the PM-controller based AQM algorithm by regarding the TCP congestion control mechanism as an input-rate based Adaptive Fuzzy Neuron PID control algorithm(IRAFNPID) to avoid congestion in TCP/AQM networks. By means of simulations, we evaluate and compare the performance of traditional PID, single neural adaptive PID(SNAPID) and IRAFNPID, simulations with experiment data analysis and find that IRAFNPID has better convergence, stability, robustness, goodput and lower loss ratio.