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Found 11 papers, 7 papers with code
Neural Exponential Stabilization of Control-affine Nonlinear Systems
Third, this parametrization and the inequality condition enable the design of contractivity-enforcing regularizers, which can be incorporated while designing the NN controller for exponential stabilization of the underlying nonlinear systems.
Neural Distributed Controllers with Port-Hamiltonian Structures
Controlling large-scale cyber-physical systems necessitates optimal distributed policies, relying solely on local real-time data and limited communication with neighboring agents.
SIMBa: System Identification Methods leveraging Backpropagation
This manuscript details the SIMBa toolbox (System Identification Methods leveraging Backpropagation), which uses well-established Machine Learning tools for discrete-time linear multi-step-ahead state-space System Identification (SI).
Stable Linear Subspace Identification: A Machine Learning Approach
Machine Learning (ML) and linear System Identification (SI) have been historically developed independently.
Modulation Classification Through Deep Learning Using Resolution Transformed Spectrograms
Modulation classification is an essential step of signal processing and has been regularly applied in the field of tele-communication.
Universal Approximation Property of Hamiltonian Deep Neural Networks
This paper investigates the universal approximation capabilities of Hamiltonian Deep Neural Networks (HDNNs) that arise from the discretization of Hamiltonian Neural Ordinary Differential Equations.
Physically Consistent Neural ODEs for Learning Multi-Physics Systems
Since IPHS models are consistent with the first and second principles of thermodynamics by design, so are the proposed Physically Consistent NODEs (PC-NODEs).
Robust Classification using Contractive Hamiltonian Neural ODEs
Since in NODEs the input data corresponds to the initial condition of dynamical systems, we show contractivity can mitigate the effect of input perturbations.
Distributed neural network control with dependability guarantees: a compositional port-Hamiltonian approach
A main challenge of NN controllers is that they are not dependable during and after training, that is, the closed-loop system may be unstable, and the training may fail due to vanishing and exploding gradients.
Neural Energy Casimir Control for Port-Hamiltonian Systems
The energy Casimir method is an effective controller design approach to stabilize port-Hamiltonian systems at a desired equilibrium.
Stability Analysis and State-Feedback Stabilization of LPV Time-Delay Systems with Piecewise Constant Parameters subject to Spontaneous Poissonian Jumps
This paper discusses the stability analysis of linear parameter varying systems with a parameter-dependent delay where the parameters are assumed to be stochastic piecewise constants under spontaneous Poissonian jumps.
