Optimal Control via Asynchronous Communication Channels

The paper is directed toward the development of a new chapter of control theory that deals with networked systems in which control and communication issues are combined together and in which the delays and limitations of the communication channels between sensors, actuators, and controllers are taken into account. We consider a situation where a single decision maker receives the sensor data and at the same time controls many linear discrete-time partially-observed subsystems perturbed by white noises via randomly delayed communication channels with finite capacities. Neither these delays nor their statistics are known in advance, but each message transmitted is equipped with a time stamp indicating the beginning time of the transfer. Under certain assumptions, a finite-horizon linear-quadratic optimal control problem is solved.     

Path Capacity Dimensioning in a Multiprotocol Label Switched Network: Analysis of Optimal and Suboptimal Solutions

In recent years, the problem of capacity allocation for a label switched patch (LSP) in a multiprotocol label switched (MPLS) network has received great attention due to its relevance in the context of traffic control. In this paper, the problem of capacity allocation is formulated as an optimal control problem and its solution is obtained by assuming the knowledge of the bandwidth requests on the entire control interval. A suboptimal solution is also given which has the advantage of requiring limited information about future bandwidth requests. The analysis of the suboptimal solution is explored both analytically and numerically by using simulated and real data. This study demonstrates that the suboptimal solution, also with limited knowledge of the future, yields a good approximation of the optimal one and requires little additional cost.     

Novel Optimal Guaranteed Cost Control of Uncertain Discrete Systems with Both State and Input Delays

The problem of the guaranteed cost control (GCC) for a class of uncertain discrete-time systems with both state and input delays is considered in this paper. A novel LMI-based approach is proposed for the existence of a state feedback controller which guarantees not only the asymptotic stability of the closed-loop system, but also an adequate performance bound over all the possible parameter uncertainties. A convex optimization algorithm is given to design the state feedback controller which minimizes a bound on a quadratic performance index. The result exhibits some favorable features in computation as shown by a numerical example. This work was supported by the National Science Foundation of China under Grants 60504012 and 60774039.     

State estimation for Markovian jumping genetic regulatory networks with random delays

In this paper, the state estimation problem is investigated for stochastic genetic regulatory networks (GRNs) with random delays and Markovian jumping parameters. The delay considered is assumed to be satisfying a certain stochastic characteristic. Meantime, the delays of GRNs are described by a binary switching sequence satisfying a conditional probability distribution. The aim of this paper is to design a state estimator to estimate the true states of the considered GRNs through the available output measurements. By using Lyapunov functional and some stochastic analysis techniques, the stability criteria of the estimation error systems are obtained in the form of linear matrix inequalities under which the estimation error dynamics is globally asymptotically stable. Then, the explicit expression of the desired estimator is shown. Finally, a numerical example is presented to show the effectiveness of the proposed results.     

State estimation of neural networks with both time-varying delays and norm-bounded parameter uncertainties via a delay decomposition approach

This paper is concerned with the state estimation problem for neural networks with both time-varying delays and norm-bounded parameter uncertainties. By employing a delay decomposition approach and a convex combination technique, we obtain less conservative delay-dependent stability criteria to guarantee the existence of desired state estimator for the delayed neural networks. Finally, numerical examples are presented to demonstrate the effectiveness of the proposed approach.     

多时滞线性切换系统的稳定性及其反馈镇定

研究了具有多时滞线性切换系统的稳定性及其反馈镇定问题,利用完备性条件、矩阵分解与二次Lyapunov泛函,给出了多时滞切换系统渐近稳定的充分条件和切换律设计方法.在此基础上,研究了这类系统的镇定控制问题,设计了保证系统时滞独立渐近镇定的控制器.     

Queueing Dynamics and Maximal Throughput Scheduling in Switched Processing Systems

We study a processing system comprised of parallel queues, whose individual service rates are specified by a global service mode (configuration). The issue is how to switch the system between various possible service modes, so as to maximize its throughput and maintain stability under the most workload-intensive input traffic traces (arrival processes). Stability preserves the job inflow–outflow balance at each queue on the traffic traces. Two key families of service policies are shown to maximize throughput, under the mild condition that traffic traces have long-term average workload rates. In the first family of cone policies, the service mode is chosen based on the system backlog state belonging to a corresponding cone. Two distinct policy classes of that nature are investigated, MaxProduct and FastEmpty. In the second family of batch policies (BatchAdapt), jobs are collectively scheduled over adaptively chosen horizons, according to an asymptotically optimal, robust schedule. The issues of nonpreemptive job processing and non-negligible switching times between service modes are addressed. The analysis is extended to cover feed-forward networks of such processing systems/nodes. The approach taken unifies and generalizes prior studies, by developing a general trace-based modeling framework (sample-path approach) for addressing the queueing stability problem. It treats the queueing structure as a deterministic dynamical system and analyzes directly its evolution trajectories. It does not require any probabilistic superstructure, which is typically used in previous approaches. Probability can be superposed later to address finer performance questions (e.g., delay). The throughput maximization problem is seen to be primarily of structural nature. The developed methodology appears to have broader applicability to other queueing systems.     

参数估计在最优化模型中的有效性及修正

证明了在涉及随机变量的最优化模型中,如果将参数估计和最优化求解过程分离,实际得到的最优化结果必然低于理论值,即参数估计在最优化模型中总是造成低效的结果。然后给出了两种可行的参数估计修正方法。     

State estimation with guaranteed performance for switching-type fuzzy neural networks in presence of sensor nonlinearities

This paper investigates the state estimation with guaranteed performance for a class of switching fuzzy neural networks. A switching-type fuzzy neural networks (STFNNs) model is proposed which captures external disturbances, sensor nonlinearities, and mode switching phenomenon of the fuzzy neural networks without the Markovian process assumption. For such a model, a state estimation problem is formulated to achieve the guaranteed performance: the estimation error system is exponentially stable with certain decay rate and a prescribed H_∞ disturbance attenuation level. A novel sufficient condition for this problem is established using the Lyapunov functional method and the average dwell time approach, and the estimator parameters are explicitly given. A numerical example is presented to show the effectiveness of the developed results.     

Modelization and Nonparametric Estimation for Dynamical Systems with Noise

We examine the effect of two specific noises (either known or small ones) on a dynamical system. We obtain consistent estimates with their rates of convergence for the invariant density in that context. We illustrate our theoretical results with simulations. This revised version was published online in August 2006 with corrections to the Cover Date.     

Optimal Control Problems with State Constraints in Fluid Mechanics and Combustion

Using nonlinear programming theory in Banach spaces we derive a version of Pontryagin's maximum principle that can be applied to distributed parameter systems under control and state constrains. The results are applied to fluid mechanics and combustion problems. Accepted 3 December 1996     

Approximate Bayesian Approach to Non-Gaussian Estimation in a Linear Model with Dependent State and Noise Vectors

This paper extends the results of Masreliez [8] on the design of non-Gaussian estimators for a more general class of the parameter estimation problem when the system state and the observation noise may be dependent and non-Gaussian simultaneously. It is shown that the proposed non-Gaussian algorithms can approximate with high precision the minimum mean square estimator. Application of the approach to the design of different optimal (and stable) estimation algorithms is illustrated. The efficiency of the proposed algorithms is tested in some simulation experiments. Accepted 5 September 2000. Online publication 26 February 2001.     

State estimation for complex networks with randomly varying nonlinearities and sensor failures

The current study is focused on the state estimator design for the discrete‐time complex networks with sensor failures and randomly varying nonlinearities. Bernoulli process is adopted to describe the randomly varying nonlinearities, and the norm‐bounded uncertain model is used to deal with the sensor failures. Then, a set of sufficient conditions are provided to guarantee that the estimation error system is stochastically stable with the prescribed property. Then, using the linear matrix inequality method, the estimator gains are obtained. Finally, the effectiveness of the proposed new design method is illustrated through a numerical example. © 2016 Wiley Periodicals, Inc. Complexity 21: 507–517, 2016     

Proxy-Users通信系统的Stackelberg模型及解的存在性

用博奕论的S tackelberg策略理论研究了P roxy-U sers通信系统.对P roxy和U sers的地位不平等的情况,建立了该系统的S tackelberg策略模型.得到了一个双层规划模型.并证明了模型的解的存在性.     

Minimax Control of Parabolic Systems with Dirichlet Boundary Conditions and State Constraints

In this paper we formulate and study a minimax control problem for a class of parabolic systems with controlled Dirichlet boundary conditions and uncertain distributed perturbations under pointwise control and state constraints. We prove an existence theorem for minimax solutions and develop effective penalized procedures to approximate state constraints. Based on a careful variational analysis, we establish convergence results and optimality conditions for approximating problems that allow us to characterize suboptimal solutions to the original minimax problem with hard constraints. Then passing to the limit in approximations, we prove necessary optimality conditions for the minimax problem considered under proper constraint qualification conditions. Accepted 7 June 1996     

Bounding blocking probabilities and throughput in queueing networks with buffer capacity constraints

We propose a new technique for upper and lower bounding of the throughput and blocking probabilities in queueing networks with buffer capacity constraints, i.e., some buffers in the network have finite capacity. By studying the evolution of multinomials of the state of the system in its assumed steady state, we obtain constraints on the possible behavior of the system. Using these constraints, we obtain linear programs whose values upper and lower bound the performance measures of interest, namely throughputs or blocking probabilities. The main advantages of this new technique are that the computational complexity does not increase with the size of the finite buffers and that the technique is applicable to systems in which some buffers have infinite capacity. The technique is demonstrated on examples taken from both manufacturing systems and communication networks. As a special case, for the M/M/s/s queue, we establish the asymptotic exactness of the bounds, i.e., that the bounds on the blocking probability asymptotically approach the exact value as the degree of the multinomials considered is increased to infinity. This revised version was published online in June 2006 with corrections to the Cover Date.     

复杂通信网络的结构分解法及其在可靠性分析中的应用

对复杂通信网络拓扑结构的恰当描述与刻画是研究其可靠性及确定信号流在其中有效的传输方式的前提。通过引进适当的数据结构并采用递归分解的技巧，本给出了将任意复杂通信网络分解为环、弦、链等基本网络结构的某种组合与连接的结构分解法，并由此得到了研究网络可靠性的新的两阶段法，即先分析各基本结构的可靠性，再由此给出原网络可靠性的估计。     

Dependence Estimation and Visualization in Multivariate Extremes with Applications to Financial Data

We investigate extreme dependence in a multivariate setting with special emphasis on financial applications. We introduce a new dependence function which allows us to capture the complete extreme dependence structure and present a nonparametric estimation procedure. The new dependence function is compared with existing measures including the spectral measure and other devices measuring extreme dependence. We also apply our method to a financial data set of zero coupon swap rates and estimate the extreme dependence in the data.AMS 2000 Subject Classification. Primary—62G32, 62H12 Secondary—62E20     

基于小波域中值滤波和运动估计的去隔行算法

本文提出了一种新的自适应去隔行算法,该方法首先将小波分解引入到去隔行算法的预处理阶段,然后利用运动估计以及混合中值滤波的特点,充分考虑相邻像素间的方向空间相关性,有效保持图像中的边界部分,并减小了运动补偿插值后的误差,尤其是对纹理信息丰富的地方得到了很好的插值效果.实验结果表明,无论是从客观上的信噪比还是主观测评来分析,图像的效果比传统的算法有了提高,能更好的满足人类对画面质量的要求.     

Estimation of nonlinear systems without a priori structural information and its application in Hammerstein model identification with dynamic nonlinearities

Email: er-wei-bai{at}uiowa.edu Received on July 30, 2005; Accepted on July 23, 2006 In the paper, we discuss identification of a nonlinear systemwithout structural information and propose two methods, thekernel method and the orthonormal basis method. The convergenceresults are established for both methods without a priori structuralinformation. We then apply the results to identification ofHammerstein models with an unknown dynamic nonlinearity. Itis also shown that identification of the linear part in Hammersteinmodels is possible with no knowledge of the dynamic nonlinearity.