A co-learning approach jointly optimizes a port-Hamiltonian system model and an energy-balancing passivity-based controller from data via alternating optimization with neural networks that embed structure for guaranteed passivity and stability.
Gaussian process port-hamiltonian systems: Bayesian learning with physics prior,
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Co-Learning Port-Hamiltonian Systems and Optimal Energy-Shaping Control
A co-learning approach jointly optimizes a port-Hamiltonian system model and an energy-balancing passivity-based controller from data via alternating optimization with neural networks that embed structure for guaranteed passivity and stability.