Learning Adaptive Control for SE(3) Hamiltonian Dynamics
- URL: http://arxiv.org/abs/2109.09974v1
- Date: Tue, 21 Sep 2021 05:54:28 GMT
- Title: Learning Adaptive Control for SE(3) Hamiltonian Dynamics
- Authors: Thai Duong and Nikolay Atanasov
- Abstract summary: This paper develops adaptive geometric control for rigid-body systems, such as ground, aerial, and underwater vehicles.
We learn a Hamiltonian model of the system dynamics using a neural ordinary differential equation network trained from state-control trajectory data.
In the second stage, we design a trajectory tracking controller with disturbance compensation from an energy-based perspective.
- Score: 15.26733033527393
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Fast adaptive control is a critical component for reliable robot autonomy in
rapidly changing operational conditions. While a robot dynamics model may be
obtained from first principles or learned from data, updating its parameters is
often too slow for online adaptation to environment changes. This motivates the
use of machine learning techniques to learn disturbance descriptors from
trajectory data offline as well as the design of adaptive control to estimate
and compensate the disturbances online. This paper develops adaptive geometric
control 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 offline system identification stage, followed by an
online adaptive control stage. In the first stage, we learn a Hamiltonian model
of the system dynamics using a neural ordinary differential equation (ODE)
network trained from state-control trajectory data with different disturbance
realizations. The disturbances are modeled as a linear combination of nonlinear
descriptors. In the second stage, we design a trajectory tracking controller
with disturbance compensation from an energy-based perspective. An adaptive
control law is employed to adjust the disturbance model online proportional to
the geometric tracking errors on the SE(3) manifold. We verify our adaptive
geometric controller for trajectory tracking on a fully-actuated pendulum and
an under-actuated quadrotor.
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