Perron root bounding for nonnegative persymmetric matrices

The eigenvalue problem for a symmetric persymmetric matrix can be reduced to two symmetric eigenvalue problems of lower order. In this paper, we find in which of these problems the Perron root of a nonnegative symmetric persymmetric matrix lies. This is applied to bound the Perron root of such class of matrices.     

A new upper bound for the skewed structured singular value

The structured singular value (s.s.v)μ enables the study of robust stability and performance of a controller in the presence of real parametric uncertainties and complex uncertainties corresponding to neglected dynamics. In spite of the NP-hard characteristic of the problem, it is now possible to compute an interval for the s.s.v. μ using polynomial-time algorithms. The skewed s.s.v. ν was introduced by Fan and Tits in the context of robust performance analysis. The primary aim of this paper is to propose a new mixed ν upper bound, which is applicable to problems with a special, but practically important, structure. We then illustrate through a realistic missile example that certain problems naturally require the ν tool rather than the μ tool. Copyright © 1999 John Wiley & Sons, Ltd.     

关于结构奇异值的一个注记

１引言Ｄｏｙｌｅ在１９８２年提出的结构奇异值（μ）方法是分析和综合结构式不确定系统的有力工具［１,２］．基于结构奇异值分析的小μ定理［２］给出了具有多个摄动块的线性动态系统鲁棒稳定的充要条件．而鲁棒性能定理［２］则进一步地将鲁棒稳定性问题和鲁棒性能问题统一成μ分析问题．然而．我们注意到,在所有研究结构奇异值的文献中,均要求块对角摄动矩阵中每个子摄动块是方的．这一要求无疑大大限制了μ方法的应用,因为非方摄动块在系统中是经常存在的．此时对Ｄｏｙｌｅ给出的结构奇异值的上界函数［１］必须进行修正．２非方…     

A numerical method for computing the structured singular value

This article suggests a new approach to computing Doyle's structured singular value (SSV) of a matrix. The SSV is a notion important in robust control and several iteration schemes exist for approximation a solution [1,2,4,-8,10,11].Our idea is to pick a special case of the general problem, which we believe to be natural and give an algorithm for studying it based on ‘off the shelf’ packages. Once we are committed to this ‘special case’ we discuss a very general plant uncertainty problem; it embraces real as well as complex plant perturbations of many kinds. The idea is simple and we believe very natural to the problem.     

A detailed comparative analysis of all practical algorithms to compute lower bounds on the structured singular value

μ analysis is one of the most efficient techniques to evaluate the stability margins and the performance levels of linear time-invariant systems in the presence of structured time-invariant uncertainties. The exact computation of the structured singular value μ is known to be NP hard in the general case, but several methods have been developed in the last 30 years to compute accurate and reliable bounds. In this paper, all existing μ lower bound algorithms are reviewed and the most relevant ones are evaluated on a wide set of real-world benchmarks, corresponding to various fields of application, system dimensions and structures of the uncertainties. The results are thoroughly analyzed and simple improvements to the existing algorithms are proposed to approach the exact value of μ with a reasonable computation cost. Conclusions show that non-conservative values can be obtained in almost all cases. A brief extension to skew-μ analysis confirms the good results obtained in the classical μ case.     

Reliable state-space upper bounds for the peak structured singular value

A state-space method for computing upper bounds for the peak of the structured singular value over frequency for both real and complex uncertainties is presented. These bounds are based on the positivity and Popov criteria for one-sided, sector-bounded and for norm-bounded, block-structured linear uncertainty. These criteria are restated and used to derive upper bounds for the peak structured singular value by equating the feasibility of a linear matrix inequality which involves a plant state-space realization to the strict positive realness of a transfer function. Numerical examples are given to illustrate these upper bounds. © 1998 John Wiley & Sons, Ltd.     

Computation of the real structured singular value via pole migration

The paper introduces a new computationally efficient algorithm to determine a lower bound on the real structured singular value μ. The algorithm is based on a pole migration approach where an optimization solver is used to compute a lower bound on real μ independent of a frequency sweep. A distinguishing feature of this algorithm from other frequency independent one‐shot tests is that multiple localized optima (if they exist) are identified and returned from the search. This is achieved by using a number of alternative methods to generate different initial conditions from which the optimization solver can initiate its search from. The pole migration algorithm presented has also been extended to determine lower bounds for complex parametric uncertainties as well as full complex blocks. However, the results presented are for strictly real and repeated parametric uncertainty problems as this class of problem is the focus of this paper and are in general the most difficult to solve. Copyright © 2014 John Wiley & Sons, Ltd.     

由回差阵奇异值求稳定裕度的退化算法

本文研宄使用回差阵奇异值求多输入多输出(MIMO)线性定常系统稳定裕度的方法.首先对此方法进行退化分析,得到求解单输入单输出(SISO)线性定常系统稳定裕度的算法步骤.在此基础上,讨论退化所得算法与传统稳定裕度的关系;进一步地,详细分析此退化算法相比传统稳定裕度的优势,进而指出当系统的增益和相位同时变化时,系统Nyquist曲线g(jω)到临界点(-1,j0)的最短距离min|1+g(jω)|可作为一种更加合理的稳定裕度指标.最后,通过对实例进行数值仿真,说明本文所提退化算法可以克服传统稳定裕度局限性,同时与传统稳定裕度结合得到比较完整的SISO线性系统稳定裕度衡量体系.     

Off-line robust fault diagnosis using the generalized structured singular value

This paper presents a new approach to the problem of off-line model-based fault diagnosis for multivariable uncertain systems. The proposed method uses the generalized structured singular value and is based on frequency-domain model invalidation tools. A novel step-by-step methodology for off-line fault identification and symptom-aided diagnostic is developed. Fault detectability and isolability issues are discussed within the new framework. Experimental results obtained by using real data from a three-tank system are reported, showing the potential of the proposed techniques.     

Polynomial methods for the structured singular value with real parameters

We consider the structured singular value problem with real parametric uncertainty only. Using techniques from algebraic geometry, we propose two algorithms that in principle can yield the precise value of the structured singular value at a fixed frequency. Their ability to do so depends upon their ability to find all common roots to a system of polynomial equations. The first algorithm is applicable to problems with two real parameters each of multiplicity two. The second algorithm is applicable to problems with n distinct real parameters. These algorithms have proved useful in applications to aerospace control law analysis.     

Computation of the structured stability radius via matrix sign function

Robustness of perturbed state space models of the form is considered, where B, C are given matrices, A is an asymptotically stable matrix and D is the unknown perturbation matrix. An efficient algorithm to compute the complex structured stability radius, which is based on the properties of the matrix sign function, is presented. A comparison with previous algorithms shows the efficiency of the new algorithm     

Solutions to the generalized Sylvester matrix equations by a singular value decomposition

In this paper, solutions to the generalized Sylvester matrix equations AX-XF=BY and MXN-X=TY with A,M ∈R ,B,T∈R, F,N∈R and the matrices N, F being in companion form, are established by a singular value decomposition of a matrix with dimensions n×(n + pr). The algorithm proposed in this paper for the euqation AX-XF = BY does not require the controllability of matrix pair (A,B) and the restriction that A,F don’t have common eigenvalues. Since singular value decomposition is adopted, the algorithm is numerically stable and may provide great convenience to the computation of the solution to these equations, and can perform important functions in many design problems in control systems theory.     

Solutions to the generalized Sylvester matrix equations by a singular value decomposition

In this paper,solutions to the generalized Sylvester matrix equations AX-XF=BY and MXN-X=TY with A,M∈Rn×n,B,T∈Rn×n,F,N∈Rp×p and the matrices N,F being in companion form,are established by a singular value decomposition of a matrix with dimensions n×(n pr).The algorithm proposed in this paper for the euqation AX-XF=BY does not require the controllability of matrix pair(A,B)andthe restriction that A,F do not have common eigenvalues.Since singular value decomposition is adopted,the algorithm is numerically stable and may provide great convenience to the computation of the solution to these equations,and can perform important functions in many design problems in control systems theory.     

An algorithm for the computation of the transfer function matrix for singular systems

For singular systems, i.e. for systems of the form $\text{E}\text{x}\text{?}\text{= Ax + Bu}$, with E singular, the problem of computing the transfer function matrix has been solved. An algorithm is developed which uses the Souriau-Frame-Faddeev algorithm for regular systems. The final expression is suitable for computer use.     

基于截断奇异值低秩矩阵恢复的Canny边缘检测算法

针对Canny算法在处理噪声图像时存在的不足,为提高其准确性和鲁棒性,提出一种基于截断奇异值的低秩矩阵恢复方法,以及一种更加准确的双噪声凸优化模型和求解方法。使用经典Canny边缘检测算法作用于分解后去除冗余信息的主成分上,将图像的边缘检测转化为对主成分的边缘检测,可以在有效地去除脉冲噪声和高斯噪声干扰的同时,更好地保留边缘信息。为验证其有效性,在不同噪声浓度以及混合噪声情况下进行实验,结果分析表明,基于低秩矩阵恢复的边缘检测算法可以更好地保留完整的边缘信息,提高边缘检测的准确性及鲁棒性。     

基于奇异向量鲁棒性的水印方法的质量补偿分析

The quality compensation for the watermarking method based on the singular matrix robustness can improve the invisibility of watermarks to some extent. To optimize the compensation parameters, the paper analyzes the embedding principle of the watermarking method, finds out the relationship between pixel modifications and singular matrix changes, and studies the variation rules of pixels before and after quality compensation, thereby obtaining the optimal compensation parameters. Based on the optimal parameters, the paper further studies the relationship between quality compensation and robustness, and determines these factors that affect the compensation effect. The final experiments verify the quality compensation effect, and the comparison with similar methods proves that the proposed quality compensation method can improve the performance of watermarking.     

A graph-theoretic characterization for the rank of the transfer matrix of a structured system

In this paper structured systems are considered and the generic rank of the transfer matrix of such systems is introduced. It is shown that this rank equals the maximum number of vertex disjoint paths from the input vertices to the output vertices in the graph that can be associated to the structured system. This maximum number of disjoint paths can be calculated using techniques from combinatorics. As an application a structural version of the well-known almost disturbance decoupling problem is proposed. The results in this paper were obtained while the author was affiliated with the Centre for Mathematics and Computer Science in Amsterdam, The Netherlands.     

Computation of the robustness margin with the skewed μ tool

Methods for the direct computation of the maximal structured singular value (s.s.v.) over the frequency range require a recursive application of μ analysis. Introducing the v measure as a skewed s.s.v., we point out the problem can be solved by a single application of the v tool. A mixed v upper bound is then proposed, which provides a direct solution to the problem. The relationship between the mixed μ and v upper bounds is moreover clarified. The nonnegativity of the sensitivity of the mixed μ upper bound is finally obtained as a corollary.     

最大奇异值移位的鲁棒图像信息隐藏

为了提高图像信息隐藏的鲁棒性和不可感知性,提出了一种基于图像块最大奇异值移位的鲁棒信息隐藏算法.算法首先对图像分块并对每个图像块进行奇异值分解,根据图像块最大奇异值的大小选择合适的阈值,并对图像块的最大奇异值进行区间划分,通过将最大奇异值移位到与秘密信息比特相对应的区间实现秘密信息的嵌入.实验结果表明,该算法具有较大的嵌入容量,且在嵌入相同大小的秘密信息时与其他同类算法相比,具有更好的图像视觉质量和鲁棒性.该算法更能适应噪声环境下的信息隐藏.