Adaptive Control of SE(3) Hamiltonian Dynamics with Learned Disturbance Features
This work addresses reliable robot autonomy in changing conditions for applications like ground, aerial, and underwater vehicles, but it is incremental as it builds on existing geometric control and learning methods.
The paper tackled the problem of adaptive control for rigid-body systems under unknown disturbances by developing a geometric adaptive controller with a learned disturbance model, achieving trajectory tracking in simulations of pendulum and quadrotor systems.
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.