Real time feedback control using predictive states estimation

In this thesis, we present a real time feedback control strategy to optimize the dynamic performance of computer communication network. In previous studies [8-10], feedback delay, arising from communication delay, was shown to degrade system performance. Considering this negative impact of delay, we propose a new control law which predicts, in advance, the traffic and exercises control based on the predicted traffic. In experiments, we apple the token bucket (TB) mechanism to construct a discrete dynamic system model [8-10], in which one multiplexor, linked to all the TBs, multiplexes the conforming traffic that have been policed at TBs. We demonstrate that the improvement of the system performance by presenting the simulation results corresponding to different stochastic traffic models. The experiments and analysis in this thesis provide valuable insight for the network researchers to do traffic optimal control [35]