Physics-informed Learning for Passivity-based Tracking Control

TL;DR

This paper introduces a physics-informed, data-driven control method for port-Hamiltonian systems that provides probabilistic stability guarantees, addressing uncertainties and extending control capabilities beyond set-point regulation.

Contribution

It proposes a novel Gaussian process-based port-Hamiltonian model with a modified matching equation for improved tracking control under uncertainties.

Findings

Effective tracking control demonstrated in simulation.

Probabilistic stability and passivity guarantees established.

Addresses model uncertainties and extends beyond set-point control.

Abstract

Passivity-based control ensures system stability by leveraging dissipative properties and is widely applied in electrical and mechanical systems. Port-Hamiltonian systems (PHS), in particular, are well-suited for interconnection and damping assignment passivity-based control (IDA-PBC) due to their structured, energy-centric modeling approach. However, current IDA-PBC faces two key challenges: (i) it requires precise system knowledge, which is often unavailable due to model uncertainties, and (ii) it is typically limited to set-point control. To address these limitations, we propose a data-driven tracking control approach based on a physics-informed model, namely Gaussian process Port-Hamiltonian systems, along with the modified matching equation. By leveraging the Bayesian nature of the model, we establish probabilistic stability and passivity guarantees. A simulation demonstrates the effectiveness of our approach.