The problem of state estimation via asynchronous communication channels with irregular transmission times

We study a linear discrete-time partially observed system perturbed by white noises. The observations are transmitted to the estimator via communication channels with irregular transmission times. Various measurement signals and even parts of a given sensor output may incur independent delays; messages transferred via the channels may be lost or corrupted. The minimum variance state estimation problem is solved. It is shown that the proposed state estimator is exponentially stable under natural assumptions.     

Optimal design of networked control systems: computer control via asynchronous communication channels

We study a linear discrete-time partially observed system perturbed by white noises. The observations are transmitted to the controller, and its outputs are sent to the actuators via communication channels that provide random transmission times and may lose messages. Various signals may incur independent delays and arrive at the destination point out of order. Under certain assumptions, a recursive minimum variance state estimator is proposed. A finite horizon linear-quadratic optimal control problem is also solved.     

Event-triggered transmission for linear control over communication channels

We consider an exponentially stable closed loop interconnection between a continuous-time linear plant and a continuous-time linear controller, and we study the problem of interconnecting the plant output to the controller input through a digital channel. We propose an event-triggered transmission policy whose goal is to transmit the measured plant output information as little as possible while preserving closed-loop stability. Global asymptotic stability is guaranteed when the plant state is available or when an estimate of the state is available (provided by a classical continuous-time linear observer). Under further assumptions, the transmission policy guarantees global exponential stability of the origin.     

正则/非正则最优控制方法在最优趋同控制的应用

趋同控制已得到广泛研究并取得重要进展,但具有最优指标约束的趋同控制仍然面临挑战.基于此,研究多智能体系统最优趋同控制问题,基于正则/非正则最优控制方法提出新型分布式控制协议,使得半正定加权矩阵性能指标最小化的同时系统能够达到趋同.特别地,所设计的控制协议由两部分组成:一部分是个体状态的反馈,目的是最小化性能指标;另一部分是相邻个体之间相对状态的反馈,目的是保证趋同.     

Optimal control data scheduling with limited controller-plant communication

This paper considers optimal control data scheduling for finite-horizon linear quadratic regulation (LQR) control of scalar systems with limited controller-plant communication. Both the single-system and multiple-system scenarios are studied. For the first scenario, we derive the necessary and sufficient condition for a comparison function to be positive. Using this condition, the optimality of an explicit schedule is extended from unstable systems in the existing work to general systems. For the second scenario, we are able to construct explicit optimal scheduling policies for three particular classes of problems. Numerical examples are provided to illustrate the proposed results.     

具有多传输通道系统的网络化预测控制

本文研究了一类具有多传输通道网络化系统的控制问题,基于网络化预测控制方法,提出了一种改进型的分布式预测补偿方式,从而更有效地利用反馈数据来提高控制系统的性能.对闭环网络化预测控制系统进行分析,得到其稳定性条件,特别地,在模型精确已知和多传输通道的时延为定常的情况下,该条件将会退化为本地控制的闭环系统稳定性条件.上述结论的好处是网络化预测控制系统中状态观测器和控制器的设计可以参考本地控制.通过球杆系统算例验证本文所提方法的正确性和有效性.     

计算机病毒的最优控制模型*

提出一个带有最优控制的SAIC模型,让感染病毒的节点数目和系统消耗保持在较低的水平。运用最优控制理论的相关原理和方法,证明了最优控制的存在性,并给出了刻画最优控制的最优系统。数值仿真结果表明,使用适当的控制策略后,计算机病毒的传播得到了有效的控制。所提的方法有望成为一种有用的工具来控制计算机病毒的传播。     

Stability of model-based networked control systems with time-varying transmission times

In model-based networked control systems (MB-NCSs), an explicit model of the plant is used to produce an estimate of the plant state behavior between transmission times. In this paper, the stability of MB-NCSs is studied when the controller/actuator is updated with the sensor information at nonconstant time intervals. Networked control systems with transmission times that are varying either within a time interval or are driven by a stochastic process with identically independently distributed and Markov-chain driven transmission times are studied. Sufficient conditions for Lyapunov stability are derived. For stochastically modeled transmission times almost sure stability and mean-square sufficient conditions for stability are introduced.     

Control of mobile communication systems with time-varying channels via stability methods

Consider the forward link of a mobile communications system with a single transmitter and connecting to K destinations via randomly varying channels. Data arrives in some random way and is queued according to the K destinations until transmitted. Time is divided into small scheduling intervals. Current systems can estimate the channel (e.g, via pilot signals) and use this information for scheduling. The issues are the allocation of transmitter power and/or time and bandwidth to the various queues in a queue and channel-state dependent way to assure stability and good operation. The decisions are made at the beginning of the scheduling intervals. Stochastic stability methods are used both to assure that the system is stable and to get appropriate allocations, under very weak conditions. The choice of Lyapunov function allows a choice of the effective performance criteria. The resulting controls are readily implementable and allow a range of tradeoffs between current rates and queue lengths. The various extensions allow a large variety of schemes of current interest to be covered. All essential factors are incorporated into a "mean rate" function, so that the results cover many different systems. Because of the non-Markovian nature of the problem, we use the perturbed Stochastic Lyapunov function method, which is well adapted to such problems. The method is simple and effective.     

State estimation via limited capacity noisy communication channels

The paper addresses a state estimation problem involving communication errors and capacity constraints. Discrete-time partially observed linear systems perturbed by stochastic unbounded additive disturbances are studied. Unlike the classic theory, the sensor signals are communicated to the estimator over a limited capacity noisy digital link modeled as a stochastic discrete memoryless channel. It is shown that the capability of the noisy channel to ensure state estimation with a bounded in probability error is identical to its capability to transmit information with as small probability of error as desired. In other words, the classic Shannon capacity of the channel constitutes the boundary of the observability domain. It is shown that whenever the Shannon capacity bound is met, a reliable observation can be ensured by means of a state estimator consuming a bounded (as time progresses) computational complexity and memory per unit time. The corresponding state estimator is constructed explicitly and is based on the classic block coding approach, so that traditional block encoding–decoding procedures can be employed for its implementation. This work was supported by the Australian Research Council and the Russian Foundation for Basic Research grant 06-08-01386.     

Window flow control in computer communication networks

Computer communication networks are basically resource sharing systems and are used for an efficient exchange of information among remote users. Performance (throughput, delay and power) of a computer network depends upon the intensity of data and/or information traffic. If a network's traffic is allowed to increase beyond certain limits, its performance rapidly degrades. In order to keep data traffic under control, some restrictions are applied at various levels of computer communication networks. These restrictions are usually referred to as flow control techniques.

This paper deals with window (also called end-to-end or entry-to-exit) flow control in computer communication networks. In this method, a source node is allowed to have only a limited number of unacknowledged messages, usually termed as “window size”, at a time. Flow control, in this paper, is discussed in the context of three computer communication switching techniques: packet switching, virtual cut-through switching and quasi cut-through switching. The analysis of window flow control is based upon simulation of a typical store-and-forward computer network. Several simulation results are presented to illustrate the effects of window flow control on various computer communication switching techniques.     

Suboptimal decentralized control over noisy communication channels

In this paper we present a technique for the design of decentralized controllers for mean square stability of a large scale system with cascaded clusters of subsystems. Each subsystem is linear and time-invariant and both system and measurement are subject to Gaussian noise. For stability analysis of this system we consider the effects of Additive White Gaussian Noise (AWGN) channels used for exchanging information between subsystems.     

Complex approach to choosing algorithms of compression and error-correcting coding in digital image transmission via communication channels

The problem of choosing the algorithms of compression and error-correcting coding for transmission of digital images via communication channels is considered. The quality criteria for output images are analyzed and a technique for simulating errors (faults) in a communication channel is proposed. The possibility of considerable improvement of noise immunity of compressed images is demonstrated for the compression method based on hierarchical grid interpolation. Glumov Nikolai Ivanovich (b. 1962) graduated from Kuibyshev Aviation Institute (now Samara State Aerospace University) in 1985. In 1994, he defended his Ph.D. (engineering) thesis. At present, Glumov is a senior scientist at the Institute of Image Processing Systems, Russian Academy of Sciences. His scientific interests include image processing and recognition, image compression, and simulation of digital image formation systems. He has more than 60 publications, including 20 articles and a monograph (with co-authors). N.I. Glumov is a member of the Russian Association of Image Recognition and Analysis.     

Optimal hop-by-hop flow control in computer networks

The problem of hop-by-hop flow control in a computer network is formulated as a Markov decision process with a cost function composed of the delay of the messages and the buffer constraints. The optimal control is shown to be a linear truncated function of the state and the explicit form is found when the arrival process of the messages is a Bernoulli process. For a renewal arrival process, the long-rnn average cost of any policy with a linear truncated structure is expressed by a set of linear equations.     

H-infinity performance optimization for networked control systems with limited communication channels

This paper studies the problems of H-infinity performance optimization and controller design for continuoustime NCSs with both sensor-to-controller and controller-to-actuator communication constraints (limited communication channels). By taking the derivative character of network-induced delay into full consideration and defining new Lyapunov functions, linear matrix inequalities (LMIs)-based H-infinity performance optimization and controller design are presented for NCSs with limited communication channels. If there do not exist any constraints on the communication channels, the proposed design methods are also applicable. The merit of the proposed methods lies in their less conservativeness, which is achieved by avoiding the utilization of bounding inequalities for cross products of vectors. The simulation results illustrate the merit and effectiveness of the proposed H-infinity controller design for NCSs with limited communication channels.     

Optimal control of finite source priority queues with computer system applications

The paper deals with the derivation of optimal control rules for finite source queueing systems with preemptive resume service discipline. The three performance measures considered are: server utilization, mean queue length and throughput.

It is indicated how the results on optimal control can be applied to multiprogrammed computer systems and some numerical examples are given.     

Optimal utilization of fixed-capacity channels in feedback control

The performance limitations on the linear control of a linear plant, due to the presence of a feedback channel with finite information capacity, are considered in this paper. This situation may arise in such diverse applications as (a) the remote control of a plant, using a digital data link for feedback, (b) where quantization errors and bit errors of a digital controller may be modelled as occurring in a noisy digital channel in cascade with an ideal controller, and (c) in human-operator modelling, where the sensory feedback channels are characterized by fixed information capacity due to neural noise. The principle result obtained is that, given the state-dimension n of the plant and the channel capacity ?, reliability function E(R), and block encoding time $\text{T> >}\text{1}\text{C}$, the optimum data-rate R satisfies the equation 2R=nE(R). This rate provides the optimum tradeoff between the effects of quantization errors and message errors. It is seen that R_opt→C as n becomes large, but that good channel performance is retained provided that $\text{> >}\text{n}\text{T}$.     

Optimal control problem via neural networks

This paper attempts to propose a new method based on capabilities of artificial neural networks, in function approximation, to attain the solution of optimal control problems. To do so, we try to approximate the solution of Hamiltonian conditions based on the Pontryagin minimum principle (PMP). For this purpose, we introduce an error function that contains all PMP conditions. In the proposed error function, we used trial solutions for the trajectory function, control function and the Lagrange multipliers. These trial solutions are constructed by using neurons. Then, we minimize the error function that contains just the weights of the trial solutions. Substituting the optimal values of the weights in the trial solutions, we obtain the optimal trajectory function, optimal control function and the optimal Lagrange multipliers.     

Optimal coding-decoding for systems controlled via a communication channel

In this article, we study the problem of controlling plants over a signal-to-noise ratio (SNR) constrained communication channel. Different from previous research, this article emphasises the importance of the actual channel model and coder/decoder in the study of network performance. Our major objectives include coder/decoder design for an additive white Gaussian noise (AWGN) channel with both standard network configuration and Youla parameter network architecture. We find that the optimal coder and decoder can be realised for different network configuration. The results are useful in determining the minimum channel capacity needed in order to stabilise plants over communication channels. The coder/decoder obtained can be used to analyse the effect of uncertainty on the channel capacity. An illustrative example is provided to show the effectiveness of the results.     

具有多通道数据传输的飞行器网络控制系统故障检测

研究具有多通道数据传输的飞行器网络控制系统故障检测滤波器(FDF)设计问题.考虑每个通道存在各自的网络时延,且丢包符合Markov随机过程.首先,将系统建模为转移概率部分已知的离散Markov跳变线性系统,并设计了基于观测器的故障检测滤波器,将故障检测问题转化为H∞滤波问题;然后,利用LMI工具给出了FDF的可解条件和求解方法;最后,通过某飞行器网络控制系统的数值仿真实验验证了所提出方法的有效性.