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

TL;DR

This paper introduces a physics-informed learning framework that co-learns port-Hamiltonian system models and optimal energy-shaping controllers from trajectory data, ensuring stability and robustness.

Contribution

It develops a neural network-based co-learning approach for pH systems and controllers, embedding energy structure for interpretability and stability guarantees.

Findings

Successfully applied to pendulum systems for regulation and swing-up tasks.

Learned controllers are passive, stable, and exploit natural system dynamics.

Dissipation regularization improves robustness to model discrepancies.

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.