基于参数不敏感设计的网络拥塞控制算法

本文提出了一种新颖的主动队列管理(active queue management,AQM)策略一间隔随机早期检测(interval random earlv detection,IRED).与传统的RED机制不同,IRED的参数设计中,平均队长的门限值一最小阈值和最大阈值从固定不变的单值,变为了一个阈值区间.相对于RED的单值固定阈值的设计,IRED的适应能力和鲁棒性得到增强,在大多数的网络环境下能够保持良好的性能,特别是在网络状态出现波动和突变时,其性能比传统的AQM算法更佳.本文进一步采用了TCP-AQM的动态模型分析了使用IRED控制器的拥塞控制系统的稳定性,分析了系统的稳定裕度,并给出了稳定裕度与控制增益的关系式,从而提出了一种根据期望稳定裕度来设计阈值区间的系统化方法.最后.在NS-2仿真平台上,通过与RED和Gentle-RED种算法比较,证明IRED优越性,特别是在变负载的网络环境下的良好性能.     

Congestion tracking control for wireless TCP/AQM network via immersion and invariance

This paper deals with an immersion and invariance (I&I) control design for wireless Transmission Control Protocol (TCP) network to solve Active Queue Management (AQM) problems. With the help of this strategy, the developed control law can be used to cope with congestion tracking problems for a nonlinear wireless network model. Besides, the nonlinear congestion control law is proposed to assure that the queue length is able to track the desired queue length despite having sudden changes. The developed control design is validated in the MATLAB environment. The effectiveness of the developed control is verified through a simulation and compared with integral backstepping design, backstepping design, sliding mode design, and synergetic control design. The simulation results demonstrated that the presented strategy is not only able to solve the desired congestion tracking control problem but also provide improved transient performances.     

Controllability and observability of Boolean control networks

The controllability and observability of Boolean control networks are investigated. After a brief review on converting a logic dynamics to a discrete-time linear dynamics with a transition matrix, some formulas are obtained for retrieving network and its logical dynamic equations from this network transition matrix. Based on the discrete-time dynamics, the controllability via two kinds of inputs is revealed by providing the corresponding reachable sets precisely. Then the problem of observability is also solved by giving necessary and sufficient conditions.     

Modelling TCP congestion control dynamics in drop-tail environments

In this paper we study communication networks that employ drop-tail queueing and additive-increase multiplicative-decrease (AIMD) congestion control algorithms. We show that the theory of non-negative matrices may be employed to model such networks and to derive basic theorems concerning their behaviour.     

Improving the state estimation for optimal control of stochastic processes subject to multiplicative noise

Computational models for the neural control of movement must take into account the properties of sensorimotor systems, including the signal-dependent intensity of the noise and the transmission delay affecting the signal conduction. For this purpose, this paper presents an algorithm for model-based control and estimation of a class of linear stochastic systems subject to multiplicative noise affecting the control and feedback signals. The state estimator based on Kalman filtering is allowed to take into account the current feedback to compute the current state estimate. The optimal feedback control process is adapted accordingly. The resulting estimation error is smaller than the estimation error obtained when the current state must be predicted based on the last feedback signal, which reduces variability of the simulated trajectories. In particular, the performance of the present algorithm is good in a range of feedback delay that is compatible with the delay induced by the neural transmission of the sensory inflow.     

Power system observability and dynamic state estimation for stability monitoring using synchrophasor measurements

Growing penetration of intermittent resources such as renewable generations increases the risk of instability in a power grid. This paper introduces the concept of observability and its computational algorithms for a power grid monitored by the wide-area measurement system (WAMS) based on synchrophasors, e.g. phasor measurement units (PMUs). The goal is to estimate real-time states of generators, especially for potentially unstable trajectories, the information that is critical for the detection of rotor angle instability of the grid. The paper studies the number and siting of synchrophasors in a power grid so that the state of the system can be accurately estimated in the presence of instability. An unscented Kalman filter (UKF) is adopted as a tool to estimate the dynamic states that are not directly measured by synchrophasors. The theory and its computational algorithms are illustrated in detail by using a 9-bus 3-generator power system model and then tested on a 140-bus 48-generator Northeast Power Coordinating Council power grid model. Case studies on those two systems demonstrate the performance of the proposed approach using a limited number of synchrophasors for dynamic state estimation for stability assessment and its robustness against moderate inaccuracies in model parameters.     

Incorporating state estimation into model predictive control and its application to network traffic control

Model predictive control (MPC) is of interest because it is one of the few control design methods which preserves standard design variables and yet handles constraints. MPC is normally posed as a full-state feedback control and is implemented in a certainty-equivalence fashion with best estimates of the states being used in place of the exact state. This paper focuses on exploring the inclusion of state estimates and their interaction with constraints. It does this by applying constrained MPC to a system with stochastic disturbances. The stochastic nature of the problem requires re-posing the constraints in a probabilistic form. Using a gaussian assumption, the original problem is approximated by a standard deterministically-constrained MPC problem for the conditional mean process of the state. The state estimates’ conditional covariances appear in tightening the constraints. ‘Closed-loop covariance’ is introduced to reduce the infeasibility and the conservativeness caused by using long-horizon, open-loop prediction covariances. The resulting control law is applied to a telecommunications network traffic control problem as an example.     

状态无关主动队列管理算法博弈的Nash均衡

状态无关的主动队列管理算法处理分组时不区分分组所在的流的信息,因此在Internet中,它易于设计和部署。文中通过数学分析和仿真方法研究AQM博弈和Nash均衡存在性。假设业务流是Poisson分布的且用户可自由修改发送速率,因而有结论:Drop Tail、RED不能获得Nash均衡,CHOKe可以获得近似Nash均衡。依据判定条件,推导出一种与状态无关且具有效率的Nash均衡AQM算法。     

基于逐段约束的组播拥塞控制机制研究

提出了一种基于逐段随机早期检测（RED）的网络层组播拥塞控制机制STSR,并对其有效性进行了数学推证,证明了STSR组播拥塞控制将具有更高的敏感度,组播传输链路也将具有更高的吞吐量。利用NS 2对STSR拥塞控制进行了模拟,结果证明它能保证对TCP流的友好,并实现了较粗粒度下的分布式控制。STSR在提高网络层组播健壮性的同时,最大限度地保证了其服务效率与质量。     

Run-to-run control and state estimation in high-mix semiconductor manufacturing

In the modeling and control of semiconductor manufacturing, the control engineer must be aware of all influences on the performance of each process. Upstream processes may affect the wafer substrate in a manner that alters performance in downstream operations, and the context within which a process is run may fundamentally change the way the process behaves. Incorporating these influences into a control method ultimately leads to better predictability and improved control performance. Control threads are a way of incorporating these effects into the control of a process by partitioning historical data into groups within which the deterministic sources of variation are uniform. However, if there are many products, which require many threads to be defined, there may be insufficient data to model each thread. This multi-product–multi-tool manufacturing environment (“high-mix”) requires advanced methodologies based on state estimation and recursive least squares. Several such approaches are compared in this paper based on simulation models for a high-mix fab.     

非线性高阶RED拥塞控制算法

针对随机早期检测(RED)算法在网络拥塞不严重的时候丢包率相对较大,而在较严重的时候丢包率相对较小的问题,提出了一种非线性高阶RED拥塞控制算法,目的在于提高算法对网络拥塞的调节能力。该算法建立了一个高阶分组丢弃函数模型,在最小门限值附近丢包率缓慢增长,在最大门限值附近丢包率快速增长,有效地控制了平均队列长度。NS2仿真实验验证了改进算法可有效地提高网络性能。     

基于神经网络监督控制的拥塞控制算法研究

提出了一个基于神经网络控制的主动队列管理（AQM）算法;研究了TCP/AQM拥塞控制系统的可逆性,并利用一种神经网络监督控制结构进行了AQM算法的设计。算法由一个三层前馈结构的神经网络控制器（neural network controller,NNC）和一个反馈控制器（feedback controller,FC）组成。NNC作为一个前馈控制器,通过FC产生的教师信号进行学习,以建立被控对象的逆动力学模型。仿真结果表明,提出的算法与PI（proportional-integral）算法相比,无论在瞬态性能     

Adaptive divided difference filtering for simultaneous state and parameter estimation

A novel adaptive version of the divided difference filter (DDF) applicable to non-linear systems with a linear output equation is presented in this work. In order to make the filter robust to modeling errors, upper bounds on the state covariance matrix are derived. The parameters of this upper bound are then estimated using a combination of offline tuning and online optimization with a linear matrix inequality (LMI) constraint, which ensures that the predicted output error covariance is larger than the observed output error covariance. The resulting sub-optimal, high-gain filter is applied to the problem of joint state and parameter estimation. Simulation results demonstrate the superior performance of the proposed filter as compared to the standard DDF.     

Adaptive explicit congestion control based on bandwidth estimation for high bandwidth-delay product networks

Existing congestion control protocols for high bandwidth-delay product (HBDP) networks either suffer from efficiency degradation caused by insufficient feedback of the network state, or are difficult to deploy in real networks because they require modification in IP header to provide enough feedback information. Based on VCP, this paper proposes the VCP-BE protocol that can provide more accurate network state information by estimating available bandwidth for sender with an adaptive bandwidth estimator. The estimator can dynamically adjust its parameters (e.g., observing interval) to achieve different accuracy and responsiveness to adapt to the network state indicated by two ECN bits, without no more modification in IP header than VCP. Furthermore, when the network load is high, VCP-BE calculates the ratio of current throughput to the available bandwidth to judge if network is close to the overload state. With these fine-grained network state estimation, VCP-BE can adjust the congestion window more precisely than VCP, thus greatly improves its convergence speed of achieving high bandwidth utilization and fairness. Simulation results show that VCP-BE also outperforms MLCP, which uses seven bits for explicit feedback while VCP-BE only uses two ECN bits.     

Advanced point-mass method for nonlinear state estimation

State estimation of discrete-time nonlinear non-Gaussian stochastic systems by point-mass approach, which is based on discretization of state space by a regular grid and numerical solution of Bayesian recursive relations, is treated. The stress is laid to grid design which is crucial for estimator quality and significantly affects the computational demands of the estimator. Boundary-based grid design, thrifty convolution, and multigrid design with grid splitting and merging are proposed. The main advantages of these techniques are nonnegligible support delimitation, time-saving computation of convolution, and effective processing of multimodal probability density functions, respectively. The techniques are involved into the basic point-mass approach and a new general-purpose, more sophisticated point-mass algorithm is designed. Computational demands and estimation quality of the designed algorithm are presented and compared with the particle filter in a numerical example.     

State estimation and detectability of probabilistic discrete event systems

A probabilistic discrete event system (PDES) is a nondeterministic discrete event system where the probabilities of nondeterministic transitions are specified. State estimation problems of PDES are more difficult than those of non-probabilistic discrete event systems. In our previous papers, we investigated state estimation problems for non-probabilistic discrete event systems. We defined four types of detectabilities and derived necessary and sufficient conditions for checking these detectabilities. In this paper, we extend our study to state estimation problems for PDES by considering the probabilities. The first step in our approach is to convert a given PDES into a nondeterministic discrete event system and find sufficient conditions for checking probabilistic detectabilities. Next, to find necessary and sufficient conditions for checking probabilistic detectabilities, we investigate the “convergence” of event sequences in PDES. An event sequence is convergent if along this sequence, it is more and more certain that the system is in a particular state. We derive conditions for convergence and hence for detectabilities. We focus on systems with complete event observation and no state observation. For better presentation, the theoretical development is illustrated by a simplified example of nephritis diagnosis.     

V2G网络中基于带宽自适应的拥塞控制协议优化

V2G网络下PLC链路带宽受限、高误码率等特点导致现有的TCP NewReno拥塞控制机制缺乏对丢包类型的有效判断,将链路上由噪声干扰的随机错误丢包与网络拥塞丢包统一当做拥塞事件处理,从而造成不必要的拥塞避免,导致了低吞吐量问题.根据此问题,提出了一种基于带宽自适应的拥塞控制算法.该算法通过分组预测拥塞等级感知网络状态,由此估计可用带宽来判断丢包类型,实现了拥塞窗口自适应调节.仿真结果表明该算法在拥塞窗口的增长、吞吐量、公平性、收敛性和友好性等方面都优于现有算法,V2 G网络的吞吐量得到明显提升.     

基于PID神经元网络和内模控制的拥塞控制算法*

针对网络系统的大时滞和非线性特性,设计了一种新的拥塞控制算法,将PID神经元网络与内模控制相结合应用于主动队列管理中,并使用Lyapunov理论证明了此算法的稳定性。NS仿真结果表明,这种算法的稳态和瞬态性能都优于PID算法,并且在参数变化和负载扰动时具有很强的鲁棒性。     

Stochastic sensor activation for distributed state estimation over a sensor network

We consider distributed state estimation over a resource-limited wireless sensor network. A stochastic sensor activation scheme is introduced to reduce the sensor energy consumption in communications, under which each sensor is activated with a certain probability. When the sensor is activated, it observes the target state and exchanges its estimate of the target state with its neighbors; otherwise, it only receives the estimates from its neighbors. An optimal estimator is designed for each sensor by minimizing its mean-squared estimation error. An upper and a lower bound of the limiting estimation error covariance are obtained. A method of selecting the consensus gain and a lower bound of the activating probability is also provided.