An online RL framework schedules gains from a finite library of pre-certified controllers for nonlinear quadrotor dynamics, using state invariance and dwell-time limits to ensure safety during trajectory tracking and hover.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
eess.SY 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Online Reinforcement Learning for Safe Gain Scheduling in Nonlinear Quadrotor Control
An online RL framework schedules gains from a finite library of pre-certified controllers for nonlinear quadrotor dynamics, using state invariance and dwell-time limits to ensure safety during trajectory tracking and hover.