In-flight Wind Field Identification and Prediction of Parafoil Systems

The wind field is an essential factor that affects accurate homing and flare landing of parafoil systems. In order to obtain the ambient wind field during the descent of a parafoil system, a combination method of in-flight wind field identification and prediction is proposed. First, a wind identification method only using global position system information is derived based on the flight dynamics of parafoil systems. Then a wind field prediction model is constructed using the atmospheric dynamics, and the low-altitude wind field is predicted based on the identified wind field of high-altitude. Finally, simulations of wind field identification and prediction are conducted. The results demonstrate that the proposed method can identify the wind fields precisely and also predict the wind fields reasonably. This method can potentially be applied in practical parafoil systems to provide wind field information for homing tasks.

Soft landing control of unmanned powered parafoils in unknown wind environments

For autonomous landing powered parafoils, the ability to perform a final flare maneuver against the wind direction can generate a considerable reduction of lateral and longitudinal velocities at impact, enabling a soft landing for a safe delivery of sensible loads. To realize accurate, soft landing in the unknown wind environment, an in-flight wind identification algorithm is first proposed. The wind direction and speed can be obtained online by only using the GPS sampling data based on the recursive least square method. Moreover, the 3D trajectory tracking strategy for the powered parafoil is also established, which is globally asymptotically stable. Furthermore, the lateral trajectory tracking controller and longitudinal altitude controller based on active disturbance rejection control are presented, respectively. Eventually, results from simulations demonstrate that the proposed landing control method can effectively realize accurate soft landing in unknown wind environments with the in-flight wind identification algorithm applied in the trajectory tracking process.