Co-Learning Port-Hamiltonian Systems and Optimal Energy-Shaping Control

• URL: http://arxiv.org/abs/2604.26172v2
• Date: Tue, 05 May 2026 19:57:46 GMT
• Title: Co-Learning Port-Hamiltonian Systems and Optimal Energy-Shaping Control
• Abstract summary: We develop a physics-informed learning framework for energy-shaping control of port-Hamiltonian (pH) systems from trajectory data.<n>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.
• Score: 1.7906055170287054
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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