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

2026-04-28

AI Summary

This work addresses the joint learning of port-Hamiltonian system models and their associated energy-shaping controllers directly from trajectory data to achieve stable, interpretable, and robust control. The authors propose a physics-informed co-learning framework that parameterizes both the port-Hamiltonian dynamics and the energy-balancing passivity-based control structure using neural networks. By integrating alternating optimization, policy-aware data collection, and dissipativity regularization, the method enables, for the first time, end-to-end co-learning of the model and controller. Embedded physical priors ensure interpretability and enhance sim-to-real transfer robustness. The approach is validated on planar and torsional pendulum systems for both regulation and swing-up tasks, guaranteeing intrinsic passivity and theoretical stability of the closed-loop system.

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.

Problem

• port-Hamiltonian systems
• energy-shaping control
• passivity-based control
• physics-informed learning
• optimal control

Innovation

• port-Hamiltonian systems
• energy-shaping control
• physics-informed learning
• passivity-based control
• co-learning