A Universal Optimal Control Strategy for a Tailsitter UAV

TL;DR

This paper presents a unified optimal control framework for a tailsitter UAV that seamlessly transitions across flight modes using trajectory optimization, neural network learning, and model predictive control.

Contribution

It introduces a novel integrated control approach combining nonlinear trajectory optimization, neural network generalization, and MPC for all flight regimes of a tailsitter UAV.

Findings

MPC outperforms dynamic inversion in robustness to uncertainties.

Neural networks enable real-time transition trajectory generation.

The framework achieves safe, reliable, and efficient mode transitions.

Abstract

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.