Compositional learning of dynamical system models using port-hamiltonian neural networks
Many dynamical systems—from robots interacting with their surroundings to large-scale
multi-physics systems—involve a number of interacting subsystems. Toward the objective of …
multi-physics systems—involve a number of interacting subsystems. Toward the objective of …
Physics-guided and Energy-based Learning of Interconnected Systems: From Lagrangian to Port-Hamiltonian Systems
This paper presents a framework for physics-informed energy-based neural network (NN)
design to learn models of interconnected systems under the port-Hamiltonian (pH) …
design to learn models of interconnected systems under the port-Hamiltonian (pH) …
Stabilization of Underactuated Systems of Degree One via Neural Interconnection and Damping Assignment–Passivity Based Control
S Sánchez-Escalonilla, R Reyes-Báez… - 2022 IEEE 61st …, 2022 - ieeexplore.ieee.org
In this work, we show the potential of the universal approximation property of neural
networks in the design of interconnection and damping assignment passivity-based …
networks in the design of interconnection and damping assignment passivity-based …
Structured Deep Neural Networks-Based Backstepping Trajectory Tracking Control for Lagrangian Systems
Deep neural networks (DNN) are increasingly being used to learn controllers due to their
excellent approximation capabilities. However, their black-box nature poses significant …
excellent approximation capabilities. However, their black-box nature poses significant …
Neural Exponential Stabilization of Control-affine Nonlinear Systems
This paper proposes a novel learning-based approach for achieving exponential
stabilization of nonlinear control-affine systems. We leverage the Control Contraction Metrics …
stabilization of nonlinear control-affine systems. We leverage the Control Contraction Metrics …