Co-Learning Port-Hamiltonian Systems and Optimal Energy-Shaping Control
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| Format: | Preprint |
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2026
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| _version_ | 1866915983299444736 |
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| author | Kamboj, Ankur Dey, Biswadip Srivastava, Vaibhav |
| author_facet | Kamboj, Ankur Dey, Biswadip Srivastava, Vaibhav |
| contents | 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 system inherently passive and provably stable, and exploits passive plant dynamics without canceling the natural potential. A dissipation regularization enforces strict energy decay during training, thereby enhancing robustness to sim-to-real gaps. The proposed framework is validated on state-regulation and swing-up tasks for planar and torsional pendulum systems. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2604_26172 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Co-Learning Port-Hamiltonian Systems and Optimal Energy-Shaping Control Kamboj, Ankur Dey, Biswadip Srivastava, Vaibhav Systems and Control Artificial Intelligence Machine Learning Optimization and 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 system inherently passive and provably stable, and exploits passive plant dynamics without canceling the natural potential. A dissipation regularization enforces strict energy decay during training, thereby enhancing robustness to sim-to-real gaps. The proposed framework is validated on state-regulation and swing-up tasks for planar and torsional pendulum systems. |
| title | Co-Learning Port-Hamiltonian Systems and Optimal Energy-Shaping Control |
| topic | Systems and Control Artificial Intelligence Machine Learning Optimization and Control |
| url | https://arxiv.org/abs/2604.26172 |