中文摘要：

针对TCP网络的拥塞控制问题,提出了一种基于RBF神经网络的自适应滑模控制算法.为了简化滑模控制器的设计,将系统的各个不确定参数和非线性补偿整合成一个总的不确定.考虑到网络系统的不确定性上界很难获得,使用RBF神经网络对系统不确定的上界进行自适应学习.将RBF神经网络的输出作为不确定上界的补偿,从而消除了系统的不确定带来的影响.应用RBF神经网络设计了一个自适应滑模控制器,所设计的控制器既保证了滑动模态的存在和系统的渐近稳定性,又较好地抑制了系统不确定带来的影响.仿真结果证实了该算法具有良好的稳定性和鲁棒性.

英文摘要：

For the problem of congestion control in TCP networks,an adaptive sliding mode control algorithm is presented based on the RBF neural network.To simplify the design of the sliding mode controller,the uncertain parameters of the systems and the nonlinear compensation of the systems are incorporated into a lumped uncertainty.Since the upper bound of the system uncertainties may not easily be obtained,a RBF neural network is used to learn the upper bound of system uncertainties.And the output of the RBF neural network is used to compensate the upper bound of system uncertainties,so that the effects of the system uncertainties can be eliminated.The RBF neural network is used to design an adaptive sliding mode controller which not only ensure the existence of the sliding mode on the surface and asymptotic stability of the systems,but also eliminate the effects of the system uncertainties.Simulation results verify the favorable stability and robustness of the algorithm.