A Universal Optimal Control Strategy for a Tailsitter UAV
For UAV control engineers, this work provides a single controller that eliminates gain scheduling across flight modes, but the results are simulation-based and incremental over existing methods.
This paper presents a unified optimal control framework for a Quadrotor Biplane tailsitter UAV that operates across hover, transition, and cruise regimes. The approach uses trajectory optimization with neural networks for real-time generation and Model Predictive Control for tracking, achieving superior robustness to parameter uncertainties compared to Dynamic Inversion control.
This work develops a unified optimal control framework for a Quadrotor Biplane tailsitter UAV capable of operating seamlessly across hover, transition, and cruise flight regimes. Although the tailsitter configuration enables mechanically simple mode switching, the transition maneuver remains challenging due to strong nonlinearities and rapidly varying aerodynamics. To address this, a trajectory optimization scheme based on nonlinear programming with direct collocation is formulated, incorporating nonlinear dynamics, actuator limits, and angle-of-attack constraints. The resulting optimal trajectories are safe, reliable, and time-efficient. For the cruise-to-hover maneuver, optimal trajectories are generated over a range of initial cruise velocities and subsequently learned using feedforward multilayer neural networks. The learned model generalizes across operating conditions and enables real-time generation of constraint-satisfying transition trajectories. These trajectories provide both feedforward control inputs and reference state profiles, which are tracked using a Model Predictive Controller (MPC). The MPC eliminates the need for controller switching or gain scheduling across flight envelopes, enabling a single universal controller for hover, transition, and cruise. A nonlinear Dynamic Inversion (DI) controller is also designed for comparison. Two numerical schemes for MPC are implemented and evaluated. Simulation results across all flight modes demonstrate that MPC achieves superior robustness to parameter uncertainties compared to DI. A computational cost analysis further highlights the trade-off between execution time and performance for the different MPC solvers.