广义时变系统的鲁棒容错控制

针对广义时变系统的鲁棒容错控制问题,基于矩阵不等式及建立Lyapunov方程的方法,首先,在系统出现执行器故障的情况下,分别给出了两种广义时变系统模型鲁棒镇定的充要条件.接着,在系统出现外部干扰的情况下,给出了相应模型鲁棒镇定的充分条件,同时给出了相应的容错控制器的设计方法.     

Quadratic optimization problems in robust beamforming

This paper presents a class of constrained optimization problems whereby a quadratic cost function is to be minimized with respect to a weight vector subject to an inequality quadratic constraint on the weight vector. This class of constrained optimization problems arises as a result of a motivation for designing robust antenna array processors in the field of adaptive array processing. The constrained optimization problem is first solved by using the primal-dual method. Numerical techniques are presented to reduce the computational complexity of determining the optimal Lagrange multiplier and hence the optimal weight vector. Subsequently, a set of linear constraints or at most linear plus norm constraints are developed for approximating the performance achievable with the quadratic constraint. The use of linear constraints is very attractive, since they reduce the computational burden required to determine the optimal weight vector.     

一般Lurie型控制系统的鲁棒绝对稳定性

本文得到一般的Ｌｕｒｉｅ类型非线性控制系统的鲁棒绝对稳定性的充分条件;同时,给出一些例子说明本文结果的有效性．     

线性不确定系统的稳定控制鲁棒界和多级稳定鲁棒控制

利用李雅普诺夫稳定性理论研究了线性不确定系统的稳定鲁棒控制问题,得到结果“任何一个稳定控制都是具有一定稳定鲁棒界的稳定控制”.进一步地,根据系统的不确定量的范围,设计了多级稳定鲁棒控制策略.最后给出一个例子说明设计步骤的可行性.     

同时考虑响应和毒性的Ⅱ期临床试验设计

肿瘤学研究常通过Ⅱ期临床试验设计来初步评价一个新的治疗方案的疗效,目前流行的方法是 Simon的两阶段试验设计.不过他只关注反应率,即对肿瘤有抑制作用的病人比率.而在实际临床中,人们既想控制肿瘤的生长,也不希望毒性的副作用大.Conaway和Petroni基于治疗方案的疗效和安全性,提出一种Ⅱ期临床试验设计方法,对响应与毒性给以相同的权重.本文提出新的想法来处理安全性与疗效之间的平衡关系,主要思想是分开控制反应率和毒性的第一类错误.在Ⅱ期临床试验阶段,由于样本量较小的缘故不大可能同时控制反应率和毒性的第一类错误,我们可以根据实际临床需要将这两个第一类错误分别确定在不同的显著性水平.比如,如果我们更关注治疗方案的毒性多过疗效,可以将毒性的第一类错误定在较低的水平(如5%),而将反应率的第一类错误定在较高的水平(如10%或15%).基于上面的思想,本文给出了寻找停止域和拒绝域的准则和样本量大小的确定公式.我们的方法较其它设计方案更稳健,而且能准确地控制感兴趣的参数(反应率或毒性)的第一类错误.另外,本文思想在概念上很直观,易于临床操作,尤其符合Ⅱ期临床试验设计小样本量的要求.     

Equal-Norm Tight Frames with Erasures

Equal-norm tight frames have been shown to be useful for robust data transmission. The losses in the network are modeled as erasures of transmitted frame coefficients. We give the first systematic study of the general class of equal-norm tight frames and their properties. We search for efficient constructions of such frames. We show that the only equal-norm tight frames with the group structure and one or two generators are the generalized harmonic frames. Finally, we give a complete classification of frames in terms of their robustness to erasures.     

产品失效率的验前分布稳健性分析

研究Bayes统计分析中利用验前信息的稳健性.首先,用一般方法研究了指数寿命型分布中失效率的验前分布的稳健性.然后利用Gamma分布函数的典型性质,并以平方损失下的后验期望损失为判别准则,讨论了失效率的最优Bayes稳健区间.给出了失效率的最优Bayes稳健点估计.     

Finding Robust Solutions Using Local Search

This paper investigates how a local search metaheuristic for continuous optimisation can be adapted so that it finds broad peaks, corresponding to robust solutions. This is relevant in problems in which uncertain or noisy data is present. When using a genetic or evolutionary algorithm, it is standard practice to perturb solutions once before evaluating them, using noise from a given distribution. This approach however, is not valid when using population-less techniques like local search and other heuristics that use local search. For those algorithms to find robust solutions, each solution needs to be perturbed and evaluated several times, and these evaluations need to be combined into a measure of robustness. In this paper, we examine how many of these evaluations are needed to reliably find a robust solution. We also examine the effect of the parameters of the noise distribution. Using a simple tabu search procedure, the proposed approach is tested on several functions found in the literature. This revised version was published online in August 2006 with corrections to the Cover Date.     

Dynamical Systems with Multiple Time-Varying Delays: Stability and Stabilizability

This paper deals with the class of uncertain systems with multiple time delays. The stability and stabilizability of this class of systems are considered. System robustness is also studied when the norm-bounded uncertainties are considered. LMI delay-dependent sufficient conditions for stability, stabilizability, and system robustness are established to check whether a system of this class is stable and/or stabilizable. Numerical examples are provided to show the usefulness of the proposed results.     

Design of Norm-Bounded and Sector-Bounded LQG Controllers for Uncertain Systems

The design of linear-quadratic-Gaussian (LQG) controllers is addressed for uncertain linear time-invariant systems that are either norm bounded or belong to a given sector. The system matrices are assumed to be affine functions of parameters confined to a convex polytopic region in the parameter space. Methods are developed for designing controllers which satisfy certain norm or sector conditions and are simultaneously optimal in the LQG sense. The resulting controllers provide robust stability as well as optimal performance.     

基于不确定需求的鲁棒应急物流系统

近年来各类突发事件和灾难频发,严重威胁到人们的生命安全,给人们经济生活带来了巨大的影响.灾难发生后,应急物流系统的效率是救援展开的保证.分析了应急物流系统的特点,采用鲁棒规划建立数学模型解决了应急物资需求不确定下如何进行应急配送中心选址和配送计划的安排,使得我们的决策能够体现最优性与鲁棒性的均衡.数值实验表明,建立的模型是符合实际的,数值结果具有较好的鲁棒性.     

Robust Optimal Service Analysis of Single-Server Re-Entrant Queues

We generalize the analysis of J.A. Ball, M.V. Day, and P. Kachroo (Mathematics of Control, Signals, and Systems, vol. 12, pp. 307–345, 1999) to a fluid model of a single server re-entrant queue. The approach is to solve the Hamilton-Jacobi-Isaacs equation associated with optimal robust control of the system. The method of staged characteristics is generalized from Ball et al. (1999) to construct the solution explicitly. Formulas are developed allowing explicit calculations for the Skorokhod problem involved in the system equations. Such formulas are particularly important for numerical verification of conditions on the boundary of the nonnegative orthant. The optimal control (server) strategy is shown to be of linear-index type. Dai-type stability properties are discussed. A modification of the model in which new customers are allowed only at a specified entry queue is considered in 2 dimensions. The same optimal strategy is found in that case as well.     

Adjustable robust solutions of uncertain linear programs

We consider linear programs with uncertain parameters, lying in some prescribed uncertainty set, where part of the variables must be determined before the realization of the uncertain parameters (``non-adjustable variables'), while the other part are variables that can be chosen after the realization (``adjustable variables'). We extend the Robust Optimization methodology ([1, 3-6, 9, 13, 14]) to this situation by introducing the Adjustable Robust Counterpart (ARC) associated with an LP of the above structure. Often the ARC is significantly less conservative than the usual Robust Counterpart (RC), however, in most cases the ARC is computationally intractable (NP-hard). This difficulty is addressed by restricting the adjustable variables to be affine functions of the uncertain data. The ensuing Affinely Adjustable Robust Counterpart (AARC) problem is then shown to be, in certain important cases, equivalent to a tractable optimization problem (typically an LP or a Semidefinite problem), and in other cases, having a tight approximation which is tractable. The AARC approach is illustrated by applying it to a multi-stage inventory management problem.Research was partially supported by the Israeli Ministry of Science grant #0200-1-98, the Israel Science Foundation founded by The Israel Academy of Sciences and Humanities, grant #683/99-10.0, and the Fund for Promotion of Research at the Technion.     

Solution Bounds for the Discrete Riccati Equation and Its Applications

In this paper, we address the estimation problem for the solution of the discrete algebraic matrix Riccati equation. Both upper and lower bounds are measured. Compared to the majority of the approaches proposed in the literature, the present results are sharper. We also apply the results obtained to solve the robust stabilization problem of discrete time-delay systems. A robust stabilizability criterion and the corresponding state feedback control law are proposed. Furthermore, the tolerable bound of the delay term is also estimated. Finally, numerical examples are given to demonstrate the applications of the results.     

General Robust-Optimization Formulation for Nonlinear Programming

Most research in robust optimization has been focused so far on inequality-only, convex conic programming with simple linear models for the uncertain parameters. Many practical optimization problems, however, are nonlinear and nonconvex. Even in linear programming, the coefficients may still be nonlinear functions of the uncertain parameters. In this paper, we propose robust formulations that extend the robust-optimization approach to a general nonlinear programming setting with parameter uncertainty involving both equality and inequality constraints. The proposed robust formulations are valid in a neighborhood of a given nominal parameter value and are robust to the first-order, thus suitable for applications where reasonable parameter estimations are available and uncertain variations are moderate. This work was supported in part by NSF Grant DMS-0405831     

Robust solutions to uncertain linear complementarity problems

In this paper,we adopt the robust optimization method to consider linear complementarity problems in which the data is not specified exactly or is uncertain,and it is only known to belong to a prescribed uncertainty set.We propose the notion of the p- robust counterpart and the p-robust solution of uncertain linear complementarity problems.We discuss uncertain linear complementarity problems with three different uncertainty sets,respectively,including an unknown-but-bounded uncertainty set,an ellipsoidal uncertainty set and an intersection-of-ellipsoids uncertainty set,and present some sufficient and necessary(or sufficient) conditions which p- robust solutions satisfy.Some special cases are investigated in this paper.     

可达到和可逼近总极小点的存在性和最优性

针对积分总极值，讨论并拓展了丰满集和丰满函数的概念，研究了拟上丰满和伪上丰满函数的总极值问题. 在总极值的变差积分最优性条件下，证明了拟上丰满函数的可达到极小点和伪上丰满函数的可逼近极小点的存在性.     

The total median in statistical quality control

In industry, most of the process observations are assumed to come from a normal population, but usually we merely want to control the process mean value. It is thus sensible to find control statistics, which are ‘robust’ to monitor the process mean, giving the expected rate of false alarms whenever that mean is close to the target value, although not under a normal regime. Simulation studies for a few symmetric and asymmetric distributions allow us to suggest the total median as a robust median estimator. We shall here analyse such a robustness, as well as the robustness of the total median chart comparatively to the sample mean chart, whenever we want to control the mean value of a symmetric underlying parent. Some indication is also provided on the comparative out‐of‐control behaviour of the two charts. Copyright © 2004 John Wiley & Sons, Ltd.     

Robust optimization revisited via robust vector Farkas lemmas

This paper provides characterizations of the weakly minimal elements of vector optimization problems and the global minima of scalar optimization problems posed on locally convex spaces whose objective functions are deterministic while the uncertain constraints are treated under the robust (or risk-averse) approach, i.e. requiring the feasibility of the decisions to be taken for any possible scenario. To get these optimality conditions we provide Farkas-type results characterizing the inclusion of the robust feasible set into the solution set of some system involving the objective function and possibly uncertain parameters. In the particular case of scalar convex optimization problems, we characterize the optimality conditions in terms of the convexity and closedness of an associated set regarding a suitable point.     

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In this article, we develop efficient robust method for estimation of mean and covariance simultaneously for longitudinal data in regression model. Based on Cholesky decomposition for the covariance matrix and rewriting the regression model, we propose a weighted least square estimator, in which the weights are estimated under generalized empirical likelihood framework. The proposed estimator obtains high efficiency from the close connection to empirical likelihood method, and achieves robustness by bounding the weighted sum of squared residuals. Simulation study shows that, compared to existing robust estimation methods for longitudinal data, the proposed estimator has relatively high efficiency and comparable robustness. In the end, the proposed method is used to analyse a real data set.