DecodEPFL/HamiltonianNet

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Created over 3 years ago
Updated almost 3 years ago

DecodEPFL/HamiltonianNet

DecodEPFL

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PyTorch implementation of Hamiltonian deep neural networks.

Hierarchical-control-in-islanded-DC-microgrids-with-flexible-structures

consensus_DCmG_simulations

Simulation files for the TCST 2021 paper "Consensus-based Current Sharing and Voltage Balancing in DC Microgrids with Exponential Loads"

attack_detection_DCmG_simulations

Simulation files for the TAC 2020 paper "A Distributed Cyber-Attack Detection Scheme With Application to DC Microgrids"

On-Existence-of-Equilibria-Voltage-Balancing-and-Current-Sharing-in-Consensus-Based-DC-Microgrids

HomotheticMPCmultistep

Accompanying code for the paper "Homothetic tube model predictive control with multi-step predictors".

DeepDisCoPH

Deep Distributed Control of Port-Hamiltonian Systems

DD_UIO_simulations

Simulation files for the L-CSS 2022 paper "Data-driven Unknown-input Observers and State Estimation"

FollowTheClairvoyant

Code to reproduce the result of the paper "Follow the Clairvoyant: an Imitation Learning Approach to Optimal Control", by Andrea Martin, Luca Furieri, Florian Dörfler, John Lygeros and Giancarlo Ferrari-Trecate

consensusability_simulations

Simulation files for the TCNS 2021 paper "On Consensusability of Linear Interconnected Multi-Agent Systems and Simultaneous Stabilization"

NodeREN

Neural Ordinary Differential Equation - Recurrent Equilibrium Networks (NODE-RENs)

DD_tracking_control_simulations

Simulation files for the manuscript "A Data-Driven Convex Programming Approach to Worst-Case Robust Tracking Controller Design"

ML4Microgrid-Matlab

Matlab code for the paper Identification of AC Networks via Online Learning. arXiv:2003.06210

Passivity-based-Voltage-and-Frequency-Stabilization-in-AC-microgrids

outputSLS

Neural-Distributed-Controllers

Jupyter Notebook

eiv-grid-id

DistributedREN

ScenarioSafeMinRegret

Code to reproduce the result of the paper "Regret Optimal Control for Uncertain Stochastic Systems", by Andrea Martin, Luca Furieri, Florian Dörfler, John Lygeros and Giancarlo Ferrari-Trecate

Contractive-Hamiltonian-Neural-ODEs

PyTorch implementation of Contractive Hamiltonian Neural ODEs

SafeMinRegret

Code to reproduce the result of the paper "Safe Control with Minimal Regret", by Andrea Martin, Luca Furieri, Florian Dörfler, John Lygeros and Giancarlo Ferrari-Trecate