Optimal trajectories generated via nonlinear programming and learned by neural networks enable a single MPC to control a tailsitter UAV across hover, transition, and cruise without controller switching.
Transition between level flight and hovering of a tail-sitter vertical takeoff and landing aerial robot.Advanced Robotics, 24:763–781, 04 2010
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A Universal Optimal Control Strategy for a Tailsitter UAV
Optimal trajectories generated via nonlinear programming and learned by neural networks enable a single MPC to control a tailsitter UAV across hover, transition, and cruise without controller switching.