Francesco Ferrante

Bio: Francesco Ferrante is an academic researcher from University of Grenoble. The author has contributed to research in topics: Exponential stability & Linear system. The author has an hindex of 11, co-authored 61 publications receiving 310 citations. Previous affiliations of Francesco Ferrante include University of Perugia & Centre national de la recherche scientifique.

A Hybrid Controller for DOS-Resilient String-Stable Vehicle Platoons

Abstract: This paper deals with the design of resilient Cooperative Adaptive Cruise Control (CACC) for homogeneous vehicle platoons in which communication is vulnerable to Denial-of-Service (DOS) attacks. We consider DOS attacks as consecutive packet dropouts. We present a controller tuning procedure based on linear matrix inequalities (LMI) that maximizes the resiliency to DOS attacks, while guaranteeing performance and string stability. The design procedure returns controller gains and gives a lower bound on the maximum allowable number of successive packet dropouts. A numerical example is employed to illustrate the effectiveness of the proposed approach.

Robust Almost Global Splay State Stabilization of Pulse Coupled Oscillators

Abstract: This paper deals with the problem of asymptotically stabilizing the splay state configuration of a network of identical pulse coupled oscillators through the design of the their phase response function. The network of pulse coupled oscillators is modeled as a hybrid system. The design of the phase response function is performed to achieve almost global asymptotic stability of a set, wherein oscillators’ phases are evenly distributed on the unit circle. To establish such a result, a novel Lyapunov function is proposed. Robustness with respect to frequency perturbation is assessed. Finally, the effectiveness of the proposed methodology is shown in an example.

Linear systems

Abstract: 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:

The sector bound approach to quantized feedback control | NOVA. The University of Newcastle's Digital Repository

Abstract: This paper studies a number of quantized feedback design problems for linear systems. We consider the case where quantizers are static (memoryless). The common aim of these design problems is to stabilize the given system or to achieve certain performance with the coarsest quantization density. Our main discovery is that the classical sector bound approach is nonconservative for studying these design problems. Consequently, we are able to convert many quantized feedback design problems to well-known robust control problems with sector bound uncertainties. In particular, we derive the coarsest quantization densities for stabilization for multiple-input-multiple-output systems in both state feedback and output feedback cases; and we also derive conditions for quantized feedback control for quadratic cost and H/sub /spl infin// performances.

Introduction To Infinite Dimensional Linear Systems Theory

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