基于RSVP与MPLS集成的QoS保障机制

文章分析了IntServ/RSVP与DiffServ这两种IP网络的QoS保障机制的原理,并对这两种体系结构进行了比较,说明了各自的优缺点,同时也指出了这两种机制的适用环境。提出了一种利用扩展RSVP协议与MPLS技术相结合的基于IP网络的QoS保障机制。最后,给出了利用RSVP协议建立标记交换路径(LSP)的步骤。利用该方法可以很好地保障IP网络的QoS。     

Markovian jump guaranteed cost congestion control strategies for large scale mobile networks with differentiated services traffic

In this paper, two novel congestion control strategies for mobile networks with differentiated services (Diff-Serv) traffic are presented, namely (i) a Markovian jump decentralized guaranteed cost congestion control strategy, and (ii) a Markovian jump distributed guaranteed cost congestion control strategy. The switchings or changes in the network topology are modeled by a Markovian jump process. By utilizing guaranteed cost control principles, the proposed congestion control schemes do indeed take into account the associated physical network resource constraints and are shown to be robust to unknown and time-varying network latencies and time delays. A set of Linear Matrix Inequality (LMI) conditions are obtained to guarantee the QoS of the Diff-Serv traffic with a guaranteed upper bound cost. Simulation results are presented to illustrate the effectiveness and capabilities of our proposed strategies. Comparisons with centralized and other relevant works in the literature focused on Diff-Serv traffic and mobile networks are also provided to demonstrate the advantages of our proposed solutions.     

对IP网络QoS问题几种解决方案的分析

本文讨论了当今计算机网络中的服务质量控制的有效方法。计算机网络发展至今,已经由传统的纯数据业务转向了兼有语音、图像、视频的多媒体网络业务,而传统的网络是提供尽力而为的服务,无法保证服务质量,这就使网络的拥塞问题日趋严重。解决网络拥塞的途径有许多,可以提高网络本身的带宽,可以减少或限制某些业务流量,可以改善网络节点路由器的处理性能。由此可见,网络服务质量（QoS）控制既是一个急待解决的问题,又是一项综合性的系统工作。     

Novel L1 neural network adaptive control architecture with guaranteed transient performance.

In this paper, we present a novel neural network (NN) adaptive control architecture with guaranteed transient performance. With this new architecture, both input and output signals of an uncertain nonlinear system follow a desired linear system during the transient phase, in addition to stable tracking. This new architecture uses a low-pass filter in the feedback loop, which consequently enables to enforce the desired transient performance by increasing the adaptation gain. For the guaranteed transient performance of both input and output signals of the uncertain nonlinear system, the L1 gain of a cascaded system, comprised of the low-pass filter and the closed-loop desired reference model, is required to be less than the inverse of the Lipschitz constant of the unknown nonlinearities in the system. The tools from this paper can be used to develop a theoretically justified verification and validation framework for NN adaptive controllers. Simulation results illustrate the theoretical findings.     

A Switch Architecture Guaranteeing QoS Provision and HOL Blocking Elimination

Both QoS support and congestion management techniques become essential to achieve good network performance in current high-speed interconnection networks. The most effective techniques traditionally considered for both issues, however, require too many resources for being implemented. In this paper we propose a new cost-effective switch architecture able to face the challenges of congestion management and, at the same time, to provide QoS. The efficiency of our proposal is based on using the resources (queues) used by RECN (an efficient Head-Of-Line blocking elimination technique) also for QoS support, without increasing queue requirements. Provided results show that the new switch architecture is able to guarantee QoS levels without any degradation due to congestion situations.     

多通道ATM 网络鲁棒拥塞控制

拥塞控制对ATM网络有效、稳定运行具有重要的作用，在单瓶颈多通道的网络模型下，基于Smith预估原理，提出一种新颖的鲁棒拥塞控制器设计方案，这种基于速率的拥塞控制可以保证ABR的服务质量(QoS)，理论分析和仿真结果表明，所提出方案收敛速度快，对网络的不确定因素具有较强的鲁棒性。     

基于模糊逻辑的高速网络自适应拥塞控制研究

基于模糊逻辑 ,利用自适应拥塞控制机制来预测高速网络 (如Internet中 )的拥塞问题 .把路由器的缓冲系统看作一个非线性离散动态系统 ,利用基于模糊逻辑的控制器来预测源端发送速率的确切值以防止拥塞的发生 .通过对参数向量的调节来估计无法预测的和具有统计波动性的网络通信量 ,并利用Lyapunov分析方法来验证闭环系统的稳定性 .最后 ,以一个仿真例子说明了所提出方法的有效性 .     

ABR流量控制中的变结构控制器

自适应比特(available bit rate,简称ABR)业务的流量控制是ATM网络中一种有效的拥塞控制机制和流量管理手段.在高速的ATM网络中,算法的简洁性在很大程度上决定着交换机的性能.尽管二进制ABR流量控制的简洁性具有相当大的吸引力,但标准的EFCI算法控制的队列长度和允许信元速率(allowed cell rate,简称ACR)却容易出现大幅振荡的现象,这势必会降低链路的利用率,严重影响交换机的性能.进而又有了相对复杂却有效的显式速率反馈机制.在此研究中,以已有的ABR流量控制模型为基础,应用概率拥塞判定机制,并借助鲁棒控制理论中滑模变结构控制器的设计方法,为ABR流量控制设计了一种新的二进制算法,避免了标准EFCI算法中非线性环节诱发的自激振荡,这对于充分发挥二进制流控算法的简洁性以及优化交换机的性能是极为有利的.仿真实验表明:二进制流量控制中的滑模变结构算法大幅度地抑制了ACR和队列的振荡,平滑了由此而引入的时延抖动,为实现ATM网络中的服务质量提供了可靠的实现机制.     

Data-based Fault Tolerant Control for Affine Nonlinear Systems Through Particle Swarm Optimized Neural Networks

In this paper, a data-based fault tolerant control (FTC) scheme is investigated for unknown continuous-time (CT) affine nonlinear systems with actuator faults. First, a neural network (NN) identifier based on particle swarm optimization (PSO) is constructed to model the unknown system dynamics. By utilizing the estimated system states, the particle swarm optimized critic neural network (PSOCNN) is employed to solve the Hamilton-Jacobi-Bellman equation (HJBE) more efficiently. Then, a data-based FTC scheme, which consists of the NN identifier and the fault compensator, is proposed to achieve actuator fault tolerance. The stability of the closed-loop system under actuator faults is guaranteed by the Lyapunov stability theorem. Finally, simulations are provided to demonstrate the effectiveness of the developed method.      

A neuro-adaptive congestion control scheme for round trip regulation

A novel neuro-adaptive congestion controller is presented, capable of regulating the per packet round trip time (RTT) around a piecewise constant desired RTT, thus achieving almost piecewise constant delay. The controller is implemented at the source and is proven robust against modeling imperfections, exogenous disturbances (UDP traffic) and delays (propagation, queueing). The notion of communication channels is introduced for throughput improvement. The analysis is nonlinear and the tools used are approximation-based control and linear-in-the-weights neural networks. The proposed controller is guaranteed to be saturated. Moreover, modifications are also provided to achieve rate reduction whenever congestion is detected. Simulation studies illustrate the performance of the proposed control scheme and compare it with other well-established congestion control mechanisms.     

ATM网络拥塞控制中PID控制器的设计

自适应比特（ABR）业务的流量控制是ATM网络中一种有效的拥塞控制机制和流量管理手段。在大规模的高速网络中，算法的简洁性对优化交换机的性能是至关重要的。尽管二进制ABR流量控制的简洁性具有相当的吸引力，但显式前向拥塞标识（Explicit Forward Congestion Indication,EFCI)算法控制的队列长度和允许信元速率（Allowed Cell Rate,ACR)大幅振荡，降低了链路利用率，严重的影响了交换机的性能，为此有了相对复杂却有效的显式速率反馈机制，在该文中，引入了拥塞的概率判定机制，并运用经典控制理论为拥塞判定概率的实量更新设计了线性的PID控制器，避免了非线性的控制规律可能诱发的系统自激振荡，在PID控制器的参数整定上，因为使用常用处受到限制，进而给出了一种基于确定稳定裕度的参数整定方法，仿真试验表明：二进制流量控制中的PID算法在保持了算法简洁性的前提下，大幅度地抑制了ACR和队列长度的振荡，提高了链路利用率，减小了队列系统引入的时延抖动，为保证ATM网络中的服务质量（Quality of Service,Qos)提供了必要的技术支持。     

Online Adaptive Approximate Optimal Tracking Control with Simplified Dual Approximation Structure for Continuous-time Unknown Nonlinear Systems

This paper proposes an online adaptive approximate solution for the infinite-horizon optimal tracking control problem of continuous-time nonlinear systems with unknown dynamics. The requirement of the complete knowledge of system dynamics is avoided by employing an adaptive identifier in conjunction with a novel adaptive law, such that the estimated identifier weights converge to a small neighborhood of their ideal values. An adaptive steady-state controller is developed to maintain the desired tracking performance at the steady-state, and an adaptive optimal controller is designed to stabilize the tracking error dynamics in an optimal manner. For this purpose, a critic neural network (NN) is utilized to approximate the optimal value function of the Hamilton-Jacobi-Bellman (HJB) equation, which is used in the construction of the optimal controller. The learning of two NNs, i.e., the identifier NN and the critic NN, is continuous and simultaneous by means of a novel adaptive law design methodology based on the parameter estimation error. Stability of the whole system consisting of the identifier NN, the critic NN and the optimal tracking control is guaranteed using Lyapunov theory; convergence to a near-optimal control law is proved. Simulation results exemplify the effectiveness of the proposed method.      

基于加权公平的DiffServ服务质量模型的研究

针对在核心节点实现的主动队列管理（AQM）以预见和防止拥塞,并且能够较公平地分配带宽,介绍了一种加权公平的主动队列管理算法,提出一个基于公平性的拥塞控制机制,即将这种AQM算法部署在可提供QoS服务的区分服务模型中,最后介绍仿真过程并分析该模型的性能．     

Discrete-time neural network output feedback control of nonlinear discrete-time systems in non-strict form

An adaptive neural network (NN)-based output feedback controller is proposed to deliver a desired tracking performance for a class of discrete-time nonlinear systems, which are represented in non-strict feedback form. The NN backstepping approach is utilized to design the adaptive output feedback controller consisting of: (1) an NN observer to estimate the system states and (2) two NNs to generate the virtual and actual control inputs, respectively. The non-causal problem encountered during the control design is overcome by using a dynamic NN which is constructed through a feedforward NN with a novel weight tuning law. The separation principle is relaxed, persistency of excitation condition (PE) is not needed and certainty equivalence principle is not used. The uniformly ultimate boundedness (UUB) of the closed-loop tracking error, the state estimation errors and the NN weight estimates is demonstrated. Though the proposed work is applicable for second order nonlinear discrete-time systems expressed in non-strict feedback form, the proposed controller design can be easily extendable to an nth order nonlinear discrete-time system.     

Robust adaptive neural network control for environmental boundary tracking by mobile robots

The paper addresses the problem of environmental boundary tracking for the nonholonomic mobile robot with uncertain dynamics and external disturbances. To do environmental boundary tracking, a reference velocity is designed for the nonholonomic mobile robot. In this paper, a radial basis function neural network (NN) is used to approximate a nonlinear function containing the uncertain model terms and the elements of the Hessian matrix of the environmental concentration function. Then, the NN approximator is combined with a robust control to construct a robust adaptive NN control for the mobile robot to track the desired environment boundary. It is proved that the tracking error can be guaranteed to converge to zero in the ultimate. Simulation results are presented to illustrate the stability of the robust adaptive control. Copyright © 2011 John Wiley & Sons, Ltd.     

Time-Varying Asymmetrical BLFs Based Adaptive Finite-Time Neural Control of Nonlinear Systems With Full State Constraints

This paper concentrates on asymmetric barrier Lyapunov functions (ABLFs) based on finite-time adaptive neural network (NN) control methods for a class of nonlinear strict feedback systems with time-varying full state constraints. During the process of backstepping recursion, the approximation properties of NNs are exploited to address the problem of unknown internal dynamics. The ABLFs are constructed to make sure that the time-varying asymmetrical full state constraints are always satisfied. According to the Lyapunov stability and finite-time stability theory, it is proven that all the signals in the closed-loop systems are uniformly ultimately bounded (UUB) and the system output is driven to track the desired signal as quickly as possible near the origin. In the meantime, in the scope of finite-time, all states are guaranteed to stay in the pre-given range. Finally, a simulation example is proposed to verify the feasibility of the developed finite time control algorithm.      

Control of a nonholonomic mobile robot using neural networks

A control structure that makes possible the integration of a kinematic controller and a neural network (NN) computed-torque controller for nonholonomic mobile robots is presented. A combined kinematic/torque control law is developed using backstepping and stability is guaranteed by Lyapunov theory. This control algorithm can be applied to the three basic nonholonomic navigation problems: tracking a reference trajectory, path following, and stabilization about a desired posture. Moreover, the NN controller proposed in this work can deal with unmodeled bounded disturbances and/or unstructured unmodeled dynamics in the vehicle. Online NN weight tuning algorithms do not require off-line learning yet guarantee small tracking errors and bounded control signals are utilized.     

一种基于神经元强化学习的网络拥塞控制方法

提出了一种基于神经元强化学习(Neuron-based Reinforcement Learning,NRL)的自适应AQM算法,采用链路速率和队列长度作为拥塞指示,可根据网络环境的变化在线自动调整神经元参数,从而保持良好的队列长度稳定性和对网络负载波动的鲁棒性.该算法结构简单、易于实现,且不依赖对象的模型.仿真结果表明,该算法尤其适合于解决复杂不确定性网络的拥塞控制问题,并具有更好的队列稳定性和鲁棒性.     

Model-free Adaptive Dynamic Programming Based Near-optimal Decentralized Tracking Control of Reconfigurable Manipulators

In this paper, a model-free near-optimal decentralized tracking control (DTC) scheme is developed for reconfigurable manipulators via adaptive dynamic programming algorithm. The proposed controller can be divided into two parts, namely local desired controller and local tracking error controller. In order to remove the normboundedness assumption of interconnections, desired states of coupled subsystems are employed to substitute their actual states. Using the local input/output data, the unknown subsystem dynamics of reconfigurable manipulators can be identified by constructing local neural network (NN) identifiers. With the help of the identified dynamics, the local desired control can be derived directly with corresponding desired states. Then, for tracking error subsystems, the local tracking error control is investigated by the approximate improved local cost function via local critic NN and the identified input gain matrix. To overcome the overall error caused by the substitution, identification and critic NN approximation, a robust compensation is added to construct the improved local cost function that reflects the overall error, regulation and control simultaneously. Therefore, the closed-loop tracking system can be guaranteed to be asymptotically stable via Lyapunov stability theorem. Two 2-degree of freedom reconfigurable manipulators with different configurations are employed to demonstrate the effectiveness of the proposed modelfree near-optimal DTC scheme.     

A self-adapting connection admission control solution for mobile WiMAX: Enabling dynamic switching of admission control algorithms based on predominant network usage profiles

WiMAX is a connection-oriented wireless network that provides QoS in metropolitan broadband communications. One important component in WiMAX QoS provisioning and management is the Connection Admission Control (CAC), which must be aware of the network conditions (e.g., user traffic demands and physical aspects). In our research, we define the association between a particular user traffic demand and a specific physical condition as a network usage profile. State-of-the-art proposals focus on optimizing CAC algorithms considering a single network usage profile; the adaptation of CAC algorithms when the predominant network usage profile changes is partially or fully neglected. In this article, we introduce a self-adapting CAC solution that, using a library of CAC algorithms, is able to switch the running algorithm according to the current network usage profile. The evaluation results, obtained through simulations, demonstrate that our self-adapting CAC solution is able to detect the changes on the predominant network usage profile. In addition, the results show how much different profiles can impact on the efficiency of CAC algorithms, thus confirming the need of switching the running CAC algorithm so that QoS can be guaranteed for the ongoing connections.