SUMMARY

Autonomous airdrop systems exhibit significant improvements over their unguided counterparts due to the addition of control mechanisms and real-time feedback. Increased landing accuracy comes with a high dependence on accurate sensor measurements (primarily GPS) and a consistent nominal system and environment. Unfortunately, airdrop systems represent a highly uncertain class of aerial vehicles where operation at off-nominal, degraded conditions is the norm and not the exception. Degraded conditions are caused by numerous effects including: parafoil rigging changes caused by canopy opening shock, parafoil damage during canopy inflation, parafoil canopy collapse, loss of GPS feedback during operation, payload mass and inertial loading imbalances, human rigging errors, etc. To ensure accurate landing capabilities in conditions ranging from nominal flight to the limits of controllability, a highly adaptive control law is introduced. To overcome changes in physical flight dynamics, a real-time adaptive estimation scheme is employed to independently identify system dynamics and control sensitivity through the use of a discrete nonlinear Hammerstein dynamic model. Based on the identified model, a general guidance algorithm for path planning is developed to handle various constraints and limitations based on flight conditions. Based on identified dynamics and control sensitivities, a feedback controller is implemented to track the desired path and steer the system to the target. In addition to maintained accuracy in damaged flight conditions, GPS loss mitigation via a radio frequency beacon based feedback signal is addressed through novel guidance, navigation, and control algorithms to handle the limited data available. Performance of the proposed new autonomous airdrop systems are based on a validated flight dynamic simulation model and extensive flight testing on a small scale autonomous parafoil and payload system.