A New Active Queue Management Algorithm Based on Self-Adaptive Fuzzy Neural-Network PID Controller

Active queue management (AQM) is a very important research area in congestion control. But the complexity and dynamic characteristic of the computer network cause the traditional PID control algorithm low adaptability to dynamic environment due to its fixed parameters. In order to overcome such shortcomings, intelligent control theory was introduced to congestion control research, and a new AQM algorithm called FAPIDNN was proposed. Fuzzy controller automatically computers the learning rate according to the current network state, and the neural network PID controller calculate the packet dropping probability based on the learning rate provided by the fuzzy controller. Simulation results show that FAPIDNN algorithm is superior to the presented PID controller on the queue stability, convergence speed and time delay.