Disturbance rejection enhancement using predictive control for the fixed-wing UAV with multiple ailerons

The performance of small fixed-wing unmanned aerial vehicles (UAVs) is easily degraded by exogenous disturbances. In an attempt to improve the performance, the structural change is made in conventional small fixed-wing UAV through segmentation of each aileron control surface into multiples. Detailed system identification experiments are performed on each aileron pair in the wind tunnel to acquire linear dynamic models for the roll attitude of the UAV. These experiments have provided the transfer functions for the aileron control surfaces based on corresponding frequency response. Three distinctive model predictive controllers are designed and deployed in real-time hardware to achieve the roll attitude control. The roll attitude control experiments are validated in wind tunnel under both normal and turbulent environments. The results show that multiple input and single output control system provides significant improvement in the roll attitude and disturbance rejection performance. The collective actuation of multiple control surfaces improves roll stability by (Formula presented.) to (Formula presented.) when compared to the single aileron pair based conventional control in the presence of turbulent flight conditions.