Control Allocation Strategy Based on Min–Max Optimization and Simple Neural Network

Servo-free tilt-rotor UAVs decouple position and attitude control without using servos, which cuts structural weight and removes the travel limits of traditional designs. In many applications—such as aerial platform operations and airborne photogrammetry—large attitude changes are required during hover. Conventional control-allocation schemes tend to distribute thrust unevenly, making actuators prone to saturation. To overcome these challenges, we propose a thrust-balancing control-allocation strategy specifically for passive-hinge tilt-rotor octocopters. The presented method integrates min–max optimization with the force decomposition (FD) algorithm, effectively handling actuator saturation while maintaining low computational complexity. Additionally, an offline-trained neural network is employed to replace the online optimization process, enabling the complete controller to operate on the flight control board without relying on an onboard computer. Simulation and experiment results confirm the effectiveness of the proposed strategy, demonstrating enhanced control performance and its practical feasibility for real-world UAV applications.