学習した外乱特徴を用いたSE(3)ハミルトン力学の適応制御
Adaptive Control of SE(3) Hamiltonian Dynamics with Learned Disturbance Features
ハミルトン型ニューラルODEで外乱モデルをオフライン学習し、幾何学的追従誤差に基づくオンライン適応制御で剛体システムの外乱を補償する手法を提案。振り子とクアッドロータの追従シミュレーションで実証した。
著者: Thai Duong, Nikolay Atanasov
分類: cs.RO, cs.LG, cs.SY, eess.SY
原文アブストラクト
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.
