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Ali Saberi

Researcher at Washington State University

Publications -  456
Citations -  11478

Ali Saberi is an academic researcher from Washington State University. The author has contributed to research in topics: Linear system & Synchronization. The author has an hindex of 51, co-authored 448 publications receiving 10959 citations. Previous affiliations of Ali Saberi include Northeastern University & University College of Engineering.

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Book ChapterDOI

Semi-global stabilization in the recoverable region: properties and computation of recoverable regions

TL;DR: In this paper, the authors consider the problem of stabilizing a closed-loop system with non-minimum-phase constraints and define the recoverable region as the set of all initial conditions that can be driven to zero by some control without violating the constraints on the state and input.
Journal ArticleDOI

Discrete-time $H_2$ and $H_\infty$ low-gain theory

TL;DR: In this article, Wang et al. generalized the H2 and Hoo algebraic Riccati equation (ARE) based methods for continuous-time linear systems to a discrete-time setting, where all input channels are not necessarily subject to saturation.
Journal ArticleDOI

Scale-free collaborative protocol design for state synchronization of multi-agent systems in presence of unknown nonuniform and arbitrarily large communication delays

TL;DR: In this paper , a scale-free design framework utilizing localized information exchange has been adopted for homogeneous networks of multi-agent systems subject to unknown, nonuniform and arbitrarily large communication delays.
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Remarks on the relationship between Lp stability and internal stability of nonlinear systems

TL;DR: In this article, the authors investigated the relationship between ℒp stability and internal stability of nonlinear systems and showed that under certain conditions, ℓp stability without finite gain implies attractivity of the equilibrium, and that local ℑp stability with finite gain imply local asymptotic stability of the origin.
Journal Article

A Flexible Algorithm for Sensor Network Partitioning and Self-partitioning Problems

TL;DR: A flexible and distributed stochastic automaton-based network partitioning algorithm that is capable of finding the optimal k-way partition with respect to a broad range of cost functions, and given various constraints, in directed and weighted graphs is proposed.