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Deep neural networks can be fragile and sensitive to small input perturbations that might cause a significant change in the output. In this letter, we employ contraction theory to improve the robustness of neural ODEs (NODEs). A dynamical system is contrac ...

Training deep neural networks (DNNs) can be difficult due to the occurrence of vanishing/explod- ing gradients during weight optimization. To avoid this problem, we propose a class of DNNs stemming from the time discretization of Hamiltonian systems. The t ...

The energy Casimir method is an effective controller design approach to stabilize port-Hamiltonian systems at a desired equilibrium. However, its application relies on the availability of suitable Casimir and Lyapunov functions, whose computation are gener ...

In the analysis and control of discrete-time linear time-invariant systems, the spectral radius of the system state matrix plays an essential role. Usually, it is assumed that system matrices are known, from which the spectral radius can be directly comput ...

Consensusability of multi-agent systems (MASs) certifies the existence of a distributed controller capable of driving the states of each subsystem to a consensus value. We study the consensusability of linear interconnected MASs (LIMASs) where, as in sever ...

This article studies the remote state estimation problem of linear time-invariant systems with stochastic event-triggered sensor schedules in the presence of packet drops between the sensor and the estimator. Due to the existence of packet drops, the Gauss ...

In this paper, we propose a suboptimal distributed LQR control method, applicable to systems coupled through both physical interconnections and the quadratic cost to be minimized. Thanks to a novel suboptimal but distributed cost-to-go matrix update that e ...

Consensusability is an important property for many multi-agent systems (MASs) as it implies the existence of networked controllers driving the states of MAS subsystems to the same value. Consensusability is of interest even when the MAS subsystems are phys ...

The branching fraction and direct CP asymmetry of the decay B +. J/.. + are measured using protonproton collision data collected with the LHCb detector at centre- of- mass energies of 7 and 8 TeV, corresponding to a total integrated luminosity of 3 fb - 1. ...

Deep Neural Networks (DNNs) training can be difficult due to vanishing and exploding gradients during weight optimization through backpropagation. To address this problem, we propose a general class of Hamiltonian DNNs (H-DNNs) that stem from the discretiz ...

Liang Xu

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Robust Classification Using Contractive Hamiltonian Neural ODEs

A unified framework for Hamiltonian deep neural networks

Neural Energy Casimir Control for Port-Hamiltonian Systems

Finite-sample-based Spectral Radius Estimation and Stabilizability Test for Networked Control Systems

On Consensusability of Linear Interconnected Multi-Agent Systems and Simultaneous Stabilization

Remote State Estimation With Stochastic Event-Triggered Sensor Schedule and Packet Drops

Suboptimal Distributed LQR Design for Physically Coupled Systems

Consensusability of linear interconnected multi-agent systems

Measurement of the branching fraction and CP asymmetry in B plus . J/.. plus decays

Hamiltonian Deep Neural Networks Guaranteeing Non-Vanishing Gradients by Design