Incremental Nonlinear Dynamic Inversion and Control Allocation for a Tilt Rotor UAV

A flight control system for a tilt rotor UAV has been synthesized based on an incremental nonlinear dynamic inversion technique. As known the effectiveness of dynamic inversion controllers depends on an accurate knowledge of the plant dynamics. Tilt rotor aircraft are flexible platforms capable of flying in a wide envelope, ranging from hover to forward flight conditions. Inherent dynamics of the vehicle are highly nonlinear in such wide envelope and thrust vectoring, required to control the aircraft in hover and transition phases, introduces dramatic cross coupling effects. A component based approach has been used to model nonlinear aircraft dynamics in the entire flight envelope and to compute local control derivatives at any flight condition. The resulting model was not affine in the control, and an incremental approach was chosen to solve the problem. Effector redundancy was managed developing a control allocation module for distributing control effort among the available actuators, based on their availability and effectiveness. A daisy chaining approach was chosen to manage redundancies and alleviate saturation problems. Control laws have been split in two loops: an outer loop controlling slower dynamics and outputting virtual controls for an inner loop controlling faster dynamics. Different piloting logics were identified for hover and forward flight conditions, although the largest possible degree of commonality was sought. A command blending strategy was devised to control the aircraft during transition phases. Three control logics have been implemented and tested, mixing first order and second order error dynamics in the inner loop, first order and second order reference models in the outer loop, kinematic relations and dynamic inversion algorithm in the outer loop. Results for all three controllers are presented, showing satisfactory tracking of reference inputs with moderate control effort. Differences between the three control logics are deemed marginal. Robustness and disturbance rejection characteristics have not been assessed in this paper. However those are known problems affecting dynamic inversion. Further research will be carried out in the future to improve robustness and noise sensitivity implementing robust outer loops based on H∞ synthesis and adding neural networks to capture modeling errors.