multi-input, multi-output feed-back control design for linear systems using the paradigms, theory, and tools of robust con-trol that have arisen during the past two decades. The book is aimed at graduate students and practicing engineers who have a basic knowledge of classical con-trol design

This paper studies robustness of multivariable systems with parametric uncertainties, and establishes a multivariable version of Edge Theorem. An illustrative example is presented.

Abstract:- This paper discusses the disturbance estimation problem for continuous-time multivariable dynamical systems with arbitrarily relative degrees. The disturbances, which are assumed bounded, refer to the combination of the external disturbances, the nonlinearities and the model

Abstract: This paper studies robustness of multivariable systems with parametric uncertainties, and establishes a multivariable version of Edge Theorem. An illustrative example is presented.

Abstract. In this paper, we propose a simple and computationally efficient method for computation of the transfer function and other characteristics of the multivariate systems. Multivariate systems are described by autoregressive moving-average (ARMA) equations. The fast Fourier transform

biological features of cellular systems. In this paper, we propose a new framework for discovering interactions between genes based on multiple expression measurements. This framework builds on the use of Bayesian networks for representing statistical dependencies. A Bayesian network is a graph-based model

MvTools, (Multivariable Tools) is a toolbox for Matlab 5.3 developed within the Department of Electrical Systems and Automation (DSEA), University of Pisa, with the aim to offering to the Matlab users (especially control engineers and control engineering students) a complete toolbox for linear

The ability to determine enclosures for the image set of nonlinear functions is pivotal to many applications in engineering. This paper presents a method for the systematic construction of el-lipsoidal extensions of factorable functions. It proceeds by lifting the ellipsoid to a higher dimen-sional space for every atom operation in the function DAG, thereby accounting for dependencies. We present theoretical results regarding the quadratic Hausdorff convergence of the computed en-closures. Moreover, we propose an efficient implementation, whereby the shape matrix of the lifted ellipsoid is stored in sparse format, and every atom operation corresponds to a sparse update in that matrix. We illustrate these developments with two numerical examples.

The set of all optimal controllers which maximize a robust stability radius for unstructured additive perturbations may be obtained using Hankel-norm approximation methods. These controllers guarantee robust stability for all perturbations which lie inside an open ball in the uncertainty space (say of radius r 1 ). Necessary and sufficient conditions are obtained for a perturbation lying on the boundary of this ball to be destabilizing for all maximally robust controllers. It is thus shown that a "worst-case direction" exists along which all boundary perturbations are destabilizing. By imposing a parametric constraint such that the permissible perturbations cannot have a "projection" of magnitude larger than (1;ffi)r 1 # 0 !ffi 1, in the most critical direction, the uncertainty region guaranteed to be stabilized by a subset of all maximally robust controllers can be extended beyond the ball of radius r 1 .Thechoice of the "best" maximally robust controller - in the sense that the uncer...

A non Monte Carlo path-integral algorithm that is particularly adept at handling nonlinear Lagrangians is extended to multivariate systems. This algorithm is particularly accurate for systems with moderate noise.