Adaptive Control of SE(3) Hamiltonian Dynamics with Learned Disturbance Features
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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Online Dynamics Learning for Predictive Control with an Application to Aerial Robots
In this work, we consider the task of improving the accuracy of dynamic models for model predictive control (MPC) in an online setting. Although prediction models can be learned and applied to model-based controllers, th…
Model Predictive ControlTransfer LearningMetaTune: Adjoint-based Meta-tuning via Robotic Differentiable Dynamics
Disturbance observer-based control has shown promise in robustifying robotic systems against uncertainties. However, tuning such systems remains challenging due to the strong coupling between controller gains and observe…
Physics-informed neural networks via stochastic Hamiltonian dynamics learning
In this paper, we propose novel learning frameworks to tackle optimal control problems by applying the Pontryagin maximum principle and then solving for a Hamiltonian dynamical system. Applying the Pontryagin maximum pri…
PositionDissipative SymODEN: Encoding Hamiltonian Dynamics with Dissipation and Control into Deep Learning
In this work, we introduce Dissipative SymODEN, a deep learning architecture which can infer the dynamics of a physical system with dissipation from observed state trajectories. To improve prediction accuracy while reduc…
Discrete-Time I&I Adaptive Interconnection and Damping Passivity-Based Control for Nonlinearly Parameterized Port-Controlled Hamiltonian Systems
In this paper, a discrete-time I&I-based adaptive IDA-PBC controller for uncertain nonlinearly parameterized port-controlled Hamiltonian systems (PCH), where the parameter uncertainties are assumed in the energy function…