A stochastic dynamic controller for random early detection queues using a Kalman filter algorithm

The stabilizing random early detection (RED) congestion control algorithm in transmission control protocol (TCP)-IP networks is a control theory problem. Significant attention has been drawn to this problem in the networking and control theory research communities. In this paper, we use a nonlinear dynamic model of the TCP RED congestion control algorithm to analyze and design active queue management (AQM) control systems. A linearized model of RED behavior around its nominal operating point which implicitly includes the delay in the control signal is derived. It is assumed that the system model is corrupted at the input and output by zero mean white Gaussian noise signals. An optimal state feedback stochastic controller is designed for the linearized model of the system in conjunction with a Kalman filter for state estimation. To illustrate the proposed design methodology, simulations results are presented and discussed. The proposed stochastic controller is applied to the nonlinear model of the system; Simulation results indicate that the proposed controller keeps the queue length bounded in an appropriate stochastic sense. Copyright © 2009 John Wiley & Sons, Ltd.

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