TCP/AWM网络系统的自适应有限时间漏斗拥塞控制

研究了具有外部扰动的TCP/AWM网络系统拥塞控制问题.首先，为了保证队列跟踪误差具有预设的暂态和稳态性能，引入漏斗误差变换对队列跟踪误差进行限制.其次，利用RBF神经网络处理系统中存在的非线性项.结合漏斗控制、有限时间控制、自适应Backstepping技术和RBF神经网络，提出了一种主动窗口管理算法，不仅保证了闭环系统的所有信号是半全局实际有限时间有界的，还使队列跟踪误差收敛到预先给定的漏斗边界内.最后，将本文所提方法与现有的两种同类算法进行了仿真对比，通过得到的仿真结果可以看出所设计的控制器使系统具有更快的收敛速度和更小的超调量，进一步验证了所提方法的可行性和优越性.

Adaptive Finite-Time Funnel Congestion Control of TCP/AWM Network Systems

The congestion control problem for TCP/AWM network systems with external disturbances was studied. Firstly， in order to ensure the queue tracking error with preassigned transient and steady-state performance， a funnel error transformation was introduced to limit queue tracking error. Secondly， RBF neural network was used to deal with the nonlinear terms in the network system. An active window management algorithm was proposed by combining funnel control， finite-time control， adaptive backstepping technique and RBF neural network. The proposed control algorithm ensures that all signals of the closed-loop system are semi-globally practically finite-time bounded， and the queue tracking error converges to the prescribed funnel boundary. Finally， the proposed method is compared with the existing two similar algorithms， and the simulation results show that the designed controller makes the system have a faster convergence speed and a smaller overshoot， which further verifies the feasibility and superiority of the proposed method.