Adaptive Control of SE(3) Hamiltonian Dynamics with Learned Disturbance Features

TL;DR

This paper introduces a geometric adaptive control method for rigid-body systems on SE(3), utilizing a learned disturbance model via Hamiltonian neural ODEs to improve trajectory tracking under disturbances.

Contribution

It develops a novel two-stage adaptive control framework combining offline disturbance learning with online geometric control for Hamiltonian systems on SE(3).

Findings

Effective disturbance compensation demonstrated in simulations.

Improved trajectory tracking accuracy achieved.

Applicable to various robotic platforms like quadrotors.

Abstract

Adaptive control is a critical component of reliable robot autonomy in rapidly changing operational conditions. Adaptive control designs benefit from a disturbance model, which is often unavailable in practice. This motivates the use of machine learning techniques to learn disturbance features from training data offline, which can subsequently be employed to compensate the disturbances online. This paper develops geometric adaptive control with a learned disturbance model for rigid-body systems, such as ground, aerial, and underwater vehicles, that satisfy Hamilton's equations of motion over the $SE(3)$ manifold. Our design consists of an \emph{offline disturbance model identification stage}, using a Hamiltonian-based neural ordinary differential equation (ODE) network trained from state-control trajectory data, and an \emph{online adaptive control stage}, estimating and compensating the disturbances based on geometric tracking errors. We demonstrate our adaptive geometric controller in trajectory tracking simulations of fully-actuated pendulum and under-actuated quadrotor systems.