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.
Hierarchical and hybrid model predictive control of quadcopter air vehicles
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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.