Balanced truncation model reduction for semidiscretized Stokes equation

We discuss model reduction of linear continuous-time descriptor systems that arise in the control of semidiscretized Stokes equations. Balanced truncation model reduction methods for descriptor systems are presented. These methods are closely related to the proper and improper controllability and observability Gramians and Hankel singular values of descriptor systems. The Gramians can be computed by solving projected generalized Lyapunov equations. Important properties of the balanced truncation approach are that the asymptotic stability is preserved in the reduced order system and there is a priori bound on the approximation error. We demonstrate the application of balanced truncation model reduction to the semidiscretized Stokes equation

Model Reduction of Descriptor Systems

Model reduction is of fundamental importance in many control applications. We consider model reduction methods for linear continuous-time descriptor systems. The methods are based on balanced truncation techniques and closely related to the controllability and observability Gramians and Hankel singular values of descriptor systems. The Gramians can be computed by solving the generalized Lyapunov equations with special right-hand sides. The numerical solution of generalized Lyapunov equations is also discussed. A numerical example is given

A Characterization of all Solutions to the Four Block General Distance Problem

All solutions to the four block general distance problem which arises in H^∞ optimal control are characterized. The procedure is to embed the original problem in an all-pass matrix which is constructed. It is then shown that part of this all-pass matrix acts as a generator of all solutions. Special attention is given to the characterization of all optimal solutions by invoking a new descriptor characterization of all-pass transfer functions. As an application, necessary and sufficient conditions are found for the existence of an H^∞ optimal controller. Following that, a descriptor representation of all solutions is derived

Passivity-preserving parameterized model order reduction using singular values and matrix interpolation

We present a parameterized model order reduction method based on singular values and matrix interpolation. First, a fast technique using grammians is utilized to estimate the reduced order, and then common projection matrices are used to build parameterized reduced order models (ROMs). The design space is divided into cells, and a Krylov subspace is computed for each cell vertex model. The truncation of the singular values of the merged Krylov subspaces from the models located at the vertices of each cell yields a common projection matrix per design space cell. Finally, the reduced system matrices are interpolated using positive interpolation schemes to obtain a guaranteed passive parameterized ROM. Pertinent numerical results validate the proposed technique

Modern CACSD using the Robust-Control Toolbox

The Robust-Control Toolbox is a collection of 40 M-files which extend the capability of PC/PRO-MATLAB to do modern multivariable robust control system design. Included are robust analysis tools like singular values and structured singular values, robust synthesis tools like continuous/discrete H(exp 2)/H infinity synthesis and Linear Quadratic Gaussian Loop Transfer Recovery methods and a variety of robust model reduction tools such as Hankel approximation, balanced truncation and balanced stochastic truncation, etc. The capabilities of the toolbox are described and illustated with examples to show how easily they can be used in practice. Examples include structured singular value analysis, H infinity loop-shaping and large space structure model reduction