Adaptive neural sliding mode control for TCP networks
Yuanwei Jing · Dianji yu kongzhi xuebao · 2012
To save the problem of congestion control in transmission control protocol(TCP) networks,by incorporating sliding mode control with radial basis function(RBF) neural networks,an active queue management algorithm is presented in presence of TCP load and round trip time which are more abrupt and time-varying.Since network system parameters are unknown and time-varying,the RBF neural networks were used to approximate the network system parameters so that the active queue management algorithm was easily implemented.The network system parameters are well estimated by updating the RBF neural network weights according to Lyapunov theory.By using the output of the RBF neural network as the sliding mode controller parameters,an active queue management algorithm was designed to guarantee the network system was asymptotically stable.Compared with proportional-integral controller and conventional sliding mode controller,simulation results show that the proposed algorithm has fast system response and steady queue length as well as better robustness under various network conditions.