1. Introduction. 2. Linear Algebra. 3. Linear Systems. 4. H2 and Ha Spaces. 5. Internal Stability. 6. Performance Specifications and Limitations. 7. Balanced Model Reduction. 8. Uncertainty and Robustness. 9. Linear Fractional Transformation. 10. m and m- Synthesis. 11. Controller Parameterization. 12. Algebraic Riccati Equations. 13. H2 Optimal Control. 14. Ha Control. 15. Controller Reduction. 16. Ha Loop Shaping. 17. Gap Metric and ...u- Gap Metric. 18. Miscellaneous Topics. Bibliography. Index.

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Essentials of Robust Control

The Theory of Matrices. By F R. Gantmacher. Two volumes, pp. 374 and 276. 1959. (Translated from the Russian by K. A. Hirsch; Chelsea Publishing Company, New York)

Many problems in the sciences and engineering can be rephrased as optimization problems on matrix search spaces endowed with a so-called manifold structure. This book shows how to exploit the special structure of such problems to develop efficient numerical algorithms. It places careful emphasis on both the numerical formulation of the algorithm and its differential geometric abstraction--illustrating how good algorithms draw equally from the insights of differential geometry, optimization, and numerical analysis. Two more theoretical chapters provide readers with the background in differential geometry necessary to algorithmic development. In the other chapters, several well-known optimization methods such as steepest descent and conjugate gradients are generalized to abstract manifolds. The book provides a generic development of each of these methods, building upon the material of the geometric chapters. It then guides readers through the calculations that turn these geometrically formulated methods into concrete numerical algorithms. The state-of-the-art algorithms given as examples are competitive with the best existing algorithms for a selection of eigenspace problems in numerical linear algebra. Optimization Algorithms on Matrix Manifolds offers techniques with broad applications in linear algebra, signal processing, data mining, computer vision, and statistical analysis. It can serve as a graduate-level textbook and will be of interest to applied mathematicians, engineers, and computer scientists.

http://www.eeci-institute.eu/GSC2011/Photos-EECI/EECI-GSC-2011-M5/book_AMS.pdf

Optimization Algorithms on Matrix Manifolds

Most of the signal processing that we will study in this course involves local operations on a signal, namely transforming the signal by applying linear combinations of values in the neighborhood of each sample point. You are familiar with such operations from Calculus, namely, taking derivatives and you are also familiar with this from optics namely blurring a signal. We will be looking at sampled signals only. Let's start with a few basic examples. Local difference Suppose we have a 1D image and we take the local difference of intensities, DI(x) = 1 2 (I(x + 1) − I(x − 1)) which give a discrete approximation to a partial derivative. (We compute this for each x in the image.) What is the effect of such a transformation? One key idea is that such a derivative would be useful for marking positions where the intensity changes. Such a change is called an edge. It is important to detect edges in images because they often mark locations at which object properties change. These can include changes in illumination along a surface due to a shadow boundary, or a material (pigment) change, or a change in depth as when one object ends and another begins. The computational problem of finding intensity edges in images is called edge detection. We could look for positions at which DI(x) has a large negative or positive value. Large positive values indicate an edge that goes from low to high intensity, and large negative values indicate an edge that goes from high to low intensity. Example Suppose the image consists of a single (slightly sloped) edge:

http://ieeexplore.ieee.org/iel5/5/31307/01456462.pdf

Linear systems

Linear Robust Control

An analogue of Banach's fixed point theorem in partially ordered sets is proved in this paper, and several applications to linear and nonlinear matrix equations are discussed.

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A fixed point theorem in partially ordered sets and some applications to matrix equations

Necessary and sufficient conditions for the existence of a positive definite solution of the matrix equation X + A*X-1A = Q

Existence and comparison theorems for algebraic Riccati equations for continuous- and discrete-time systems

This paper treats a set of equations of the form $X+A^{\star}{\cal F}(X)A =Q$, where ${\cal F}$ maps positive definite matrices either into positive definite matrices or into negative definite matrices, and satisfies some monotonicity property. Here A is arbitrary and Q is a positive definite matrix. It is shown that under some conditions an iteration method converges to a positive definite solution. An estimate for the rate of convergence is given under additional conditions, and some numerical results are given. Special cases are considered, which cover also particular cases of the discrete algebraic Riccati equation.

On an Iteration Method for Solving a Class of Nonlinear Matrix Equations

Motivating Problems, Systems and Realizations.- Motivating Problems.- Operator Nodes, Systems, and Operations on Systems.- Various Classes of Systems.- Realization and Linearization of Operator Functions.- Factorization and Riccati Equations.- Canonical Factorization and Applications.- Minimal Realization and Minimal Factorization.- Minimal Systems.- Minimal Realizations and Pole-Zero Structure.- Minimal Factorization of Rational Matrix Functions.- Degree One Factors, Companion Based Rational Matrix Functions, and Job Scheduling.- Factorization into Degree One Factors.- Complete Factorization of Companion Based Matrix Functions.- Quasicomplete Factorization and Job Scheduling.- Stability of Factorization and of Invariant Subspaces.- Stability of Spectral Divisors.- Stability of Divisors.- Factorization of Real Matrix Functions.

André C. M. Ran

Papers

A fixed point theorem in partially ordered sets and some applications to matrix equations

Necessary and sufficient conditions for the existence of a positive definite solution of the matrix equation X + A*X-1A = Q

Existence and comparison theorems for algebraic Riccati equations for continuous- and discrete-time systems

On an Iteration Method for Solving a Class of Nonlinear Matrix Equations

Factorization of Matrix and Operator Functions: The State Space Method