Showing papers by "Francesco Amato published in 2009"

Stabilization of Bilinear Systems Via Linear State-Feedback Control

Abstract: This paper deals with the problem of stabilizing a bilinear system via linear state-feedback control. The proposed procedures enable us to compute a static state-feedback controller such that the zero-equilibrium point of the closed-loop system is asymptotically stable; moreover, it ensure that an assigned polytopic region is enclosed into the domain of attraction of the equilibrium point. The controller design requires the solution of a convex optimization problem involving linear matrix inequalities. The applicability of the technique is illustrated through an example, dealing with the design of a controller for a Cuk dc-dc converter.

Input-output finite-time stability of linear systems

Abstract: When only the input-output behavior of a dynamical system is of concern, usually Bounded-Input Bounded-Output (BIBO) stability is studied, for which several results exist in literature. The present paper investigates the analogous concept in the framework of Finite Time Stability (FTS), namely the Input-Output FTS. A system is said to be IO finite time stable if, assigned a bounded input class and some boundaries in the output signal space, the output never exceeds such boundaries over a prespecified (finite) interval of time. FTS has been already investigated in several papers in terms of state boundedness, whereas this is the first work dealing with the characterization of the input-output behavior. Sufficient conditions are given, concerning the class of L 2 and L ∞ input signals, for the analysis of IO-FTS and for the design of a static state feedback controller, guaranteeing IO-FTS of the closed loop system. Finally, the applicability of the results is illustrated by means of two numerical examples.

Robust stability via polyhedral Lyapunov functions

Abstract: In this paper we study the robustness analysis problem for linear continuous-time systems subject to parametric time-varying uncertainties making use of piecewise linear (polyhedral) Lyapunov functions. A given class of Lyapunov functions is said to be “universal” for the uncertain system under consideration if the search of a Lyapunov function that proves the robust stability of the system can be restricted, without conservatism, to the elements of the class. In the literature it has been shown that the class of polyhedral functions is universal, while, for instance, the class of quadratic Lyapunov functions is not. This fact justifies the effort of developing efficient algorithms for the construction of optimal polyhedral Lyapunov functions. In this context, we provide a novel procedure that enables to construct, in the general n-dimensional case, a polyhedral Lyapunov function to prove the robust stability of a given system. Some numerical examples are included, where we show the effectiveness of the proposed approach comparing it with other approaches proposed in the literature.

Exploiting prior knowledge and preferential attachment to infer biological interaction networks

Abstract: The problem of reverse-engineering the topology of interaction networks from time-course experimental data has received a considerable attention in the literature, due to the potential applications in the most diverse fields, comprising engineering, biology, economics and social sciences. An important insight was brought by the introduction of the concept of scale-free topology, whose implications have been widely discussed in literature over the last decade. The aim of this work is to investigate whether it is possible to improve the performances of an inference technique, based on dynamical linear systems and LMI-based optimization, by exploiting the same mechanisms that underpin scale-free networks generation, i.e. growth and preferential attachment (PA). The work is prominently concerned with applications in the biological domain, though the algorithm can be in principle adapted also to other frameworks. A statistical evaluation is performed, by using numerically simulated networks, showing that the growth and PA mechanisms actually improve the inference power of the considered technique. Finally the method is applied to a biological case-study, validating the results against experimental data available in literature.

A novel portable device for pathological voice analysis

Abstract: This paper presents a novel implementation of a DSP based acquisition and elaboration system for vocal signals. The proposed system performs real time spectro-acustic analysis of acquired voice samples and gives a visual feedback to patients alerting them about the eventual presence of anomalies. The prototype will be used for rehabilitation purpose (after medical treatment or surgery) or prevention purpose (early diseases detection).

Sufficient conditions for finite-time stability and stabilization of nonlinear quadratic systems

Abstract: In this paper we deal with the finite-time stability problem for quadratic systems. Such class of systems plays an important role in the modeling of a wide class of nonlinear processes (electrical, robotic, biological, etc.). The main results of the paper consist of two sufficient conditions for finite-time stability analysis and finite-time stabilization via static state feedback; both conditions are given in terms of the feasibility of a convex optimization problem, involving linear matrix inequalities. A numerical example illustrates the applicability of the proposed technique.

Inferring gene regulatory networks with partially known scale-free topology

Abstract: The problem of reverse-engineering the topology of interaction networks from time-course experimental data has received a considerable attention in the literature, due to the potential applications in the most diverse fields, comprising engineering, biology, economics and social sciences. An important insight was brought by the introduction of the concept of scale-free topology, whose implications have been widely discussed in literature over the last decade. The aim of this work is to investigate whether it is possible to improve the performances of an inference technique, based on dynamical linear systems and LMI-based optimization, by exploiting the same mechanisms that underpin scale-free networks generation, i.e. growth and preferential attachment (PA). The work is prominently concerned with applications in the biological domain, though the algorithm can be in principle adapted also to other frameworks. A statistical evaluation is performed, by using numerically simulated networks, showing that the growth and PA mechanisms actually improve the inference power of the considered technique.