Distributed Tracking Synchronization Protocol for a Networked of Leader-follower Unmanned Aerial Vehicles with Uncertainty

Abstract

This work investigates robust asymptotic consensus tracking problems for a group of cloud-connected leader-follower unmanned aerial vehicles with uncertainty. The protocols for attitude and position subsystems dynamics are constructed by using the states of the local and neighboring vehicles provided that they are connected by local area networks. Robust adaptive learning algorithms are also integrated with both protocols to learn and adapt to the modeling errors and external disturbance uncertainties. Lyapunov method and Graph theory use to prove that the proposed protocol allows the vehicles to reach an agreement with follower vehicles and track the states of the leader vehicle asymptotically. Convergence analysis shows that consensus protocol can force the states of the follower MAVs to track the state of the leader MAV asymptotically. The protocol designs are simple and easy to implement as they do not need the exact bound of the uncertainty that appears from external disturbances and the modeling errors. The design does not require the bound of the input of the leader vehicle. The protocol design can ensure faster and robust consensus in the presence of uncertainty as opposed to the convergence of other asymptotic consensus designs.