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Found 5 papers, 5 papers with code
A PAC-Bayesian Framework for Optimal Control with Stability Guarantees
Based on these bounds, we propose a new method for designing optimal controllers, offering a principled way to incorporate prior knowledge into the synthesis process, which aids in improving the control policy and mitigating overfitting.
Unconstrained Parametrization of Dissipative and Contracting Neural Ordinary Differential Equations
We validate the properties of NodeRENs, including the possibility of handling irregularly sampled data, in a case study in nonlinear system identification.
Neural System Level Synthesis: Learning over All Stabilizing Policies for Nonlinear Systems
We address the problem of designing stabilizing control policies for nonlinear systems in discrete-time, while minimizing an arbitrary cost function.
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.
Hamiltonian Deep Neural Networks Guaranteeing Non-vanishing Gradients by Design
Deep Neural Networks (DNNs) training can be difficult due to vanishing and exploding gradients during weight optimization through backpropagation.
