Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Model Predictive Control for Quadrotors   Aerial robots can enhance their safe and agile navigation in complex and
cluttered environments by efficiently exploiting the information collected
during a given task. In this paper, we address the learning model predictive
control problem for quadrotors. We design a learning receding--horizon
nonlinear control strategy directly formulated on the system nonlinear manifold
configuration space SO(3)xR^3. The proposed approach exploits past successful
task iterations to improve the system performance over time while respecting
system dynamics and actuator constraints. We further relax its computational
complexity making it compatible with real-time quadrotor control requirements.
We show the effectiveness of the proposed approach in learning a minimum time
control task, respecting dynamics, actuators, and environment constraints.
Several experiments in simulation and real-world set-up validate the proposed
approach.

22
The categories of this paper are: chao-dyn
9
The categories of this paper are: nlin
7
The categories of this paper are: hep-th
19
The categories of this paper are: eess
29
The categories of this paper are: funct-an
37
The categories of this paper are: supr-con
2
The categories of this paper are: math
31
The categories of this paper are: ao-sci
15
The categories of this paper are: nucl-ex
17
The categories of this paper are: q-fin
16
The categories of this paper are: stat
35
The categories of this paper are: mtrl-th
33
The categories of this paper are: plasm-ph
20
The categories of this paper are: acc-phys
27
The categories of this paper are: comp-gas
8
The categories of this paper are: hep-ex