Results 171 to 180 of about 17,271 (212)

Structured singular values with nondiagonal structures. I. Characterizations

IEEE Transactions on Automatic Control, 1996
[Part II is reviewed below (see Zbl 0879.93013).] The structured singular value is used is robustness analysis and design of feedback systems with multiple sources of modeling uncertainty. Here a generalized structure singular value is introduced and studied as a robust stability measure for a general class of uncertainties.
M K H Fan
exaly   +2 more sources

Structured singular values with nondiagonal structures. II. Computation

IEEE Transactions on Automatic Control, 1996
This is a continuation of the paper on generalized structured singular values [the authors, IEEE Trans. Autom. Control 41, No. 10, 1507-1511 (1996; Zbl 0879.93012)]. Here a numerical method for calculating this value is formulated. It consists of computing an upper norm bound scaled by a similarity transformation. This bound is equal to the generalized
M K H Fan
exaly   +3 more sources

On structured singular values of reciprocal matrices

Systems and Control Letters, 1995
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Shigeru Yamamoto
exaly   +2 more sources

Non-negative matrices and their structured singular values

Izvestiya Vysshikh Uchebnykh Zavedenii. Matematika, 2023
In this article, we present new results for the computation of structured singular values of non-negative matrices subject to pure complex perturbations. We prove the equivalence of structured singular values and spectral radius of perturbed matrix (M∆).
Rehman, M., Rasulov, T., Aminov, B.
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Fixed Structure Computation of the Structured Singular Value

1993 American Control Conference, 1993
This paper addresses the robest stability and performance analysis problem using a fixed structure approach of the structured singular value. Specifically, using recent results on H 2 / H ? , a Riccati equation approach is formulated for complex-? with constant D-scales along with an H 2 performance bound.
Wassim M. Haddad, Emmanuel G. Collins, Raymond Moser   +2 more
openaire   +1 more source

Conservatism of randomized structured singular value

IEEE Transactions on Automatic Control, 2002
In this note, we study the conservatism of structured singular value computation by a randomized algorithm. It is proved that, if the maximization problem μ(M) = max ρ(MΔ) is solved by generating polynomial number of random Δ samples and then taking the maximum of the function at these sample points, for any fixed lower bound on the confidence level ...
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Accurate Singular Value Decompositions of Structured Matrices

SIAM Journal on Matrix Analysis and Applications, 2000
The author presents a new algorithm for singular value decomposition (SVD) with computational cost \(O(n^3)\) and high relative accuracy, in contrast to conventional numerical algorithms which compute SVD with high absolute accuracy. The new algorithm consists of two general steps: (i) computation of rank-revealing decomposition; (ii) computation of ...
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On the Structure of Generalized Singular Value and QR Decompositions

SIAM Journal on Matrix Analysis and Applications, 1994
The author has previously generalized both singular value decomposition and \(QR\) decomposition from a single matrix to a chain of matrices. Here he obtains formulas for the sizes of the blocks which occur.
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Structured Singular Value: µ

1999
The linear fractional transformation (LFT) is a common framework suitable for robust stability analysis using arguments based on the small gain theorem. An LFT is an interconnection of operators arranged in a feedback configuration. These operators may be constant matrices, time-domain state-space systems, or frequency-varying transfer functions ...
Rick Lind, Marty Brenner
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The real structured singular value is hardly approximable

IEEE Transactions on Automatic Control, 1997
Given a matrix \(M\in C^{n \times n}\) and a set described by \(\Delta= \{\Delta= \text{diag} \{\delta_1 I_{k_1}, \dots, \delta_m I_{k_m}\}\); \(\delta_i\in R\), \(k_i>0\), \(\sum^m_{i=1} k_i=n\) the real \(\mu\) problem is to compute the value \(\mu=: \mu_\Delta (M)=(\inf \{\rho>0; \text{det} (I_n- \Delta M) =0\), \(\Delta \in\rho B(\Delta)\})^{-1}\),
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