AI RESEARCH
Co-Learning Port-Hamiltonian Systems and Optimal Energy-Shaping Control
arXiv CS.AI
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ArXi:2604.26172v1 Announce Type: cross 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.