LAUSR

Quadrotor Unmanned Aerial Vehicles (UAVs) are generally underactuated systems and when load is attached for transportation purposes, the system complexity increases. Therefore, the need to appropriately control such systems becomes paramount as they usually navigate in cluttered environments. In this work, we conceptualize the problem of cooperative tracking control for a Multi-agent UAV load system (MUAVLs) whereby each UAV is divided into global position and local attitude subsystems. To ensure that formation is maintained in a desired path, Neural Network Graph-theoretic Distributed Adaptive Control (NNGDAC) is used for the position subsystem with a modified virtual force artificial potential field for obstacle avoidance. Another Adaptive Feedback Linearization (AFBL) controller is also designed for the attitude subsystem which is verified by simulation results.