Co-Learning Port-Hamiltonian Systems and Optimal Energy-Shaping Control
We develop a physics-informed learning framework for energy-shaping control of port-Hamiltonian (pH) systems from trajectory data. The proposed approach co-learns a pH system model and an optimal energy-balancing passivity-based controller (EB-PBC) through alternating optimization with policy-aware data collection. At each iteration, the system model is refined using trajectory data collected under the current control policy, and the controller is re-optimized on the updated model. Both components are parameterized by neural networks that embed the pH dynamics and EB-PBC structure, ensuring interpretability in terms of energy interactions. The learned controller renders the closed-loop sy...
| Authors | Ankur Kamboj, Biswadip Dey, Vaibhav Srivastava |
|---|---|
| Primary category | eess.SY |
| Published | 28 April 2026 |
| Last updated at source | 5 May 2026 |
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