  In this paper we propose a novel distributed model predictive control (DMPC)
based algorithm with a trajectory predictor for a scenario of landing of
unmanned aerial vehicles (UAVs) on a moving unmanned surface vehicle (USV). The
algorithm is executing DMPC with exchange of trajectories between the agents at
a sufficient rate. In the case of loss of communication, and given the sensor
setup, agents are predicting the trajectories of other agents based on the
available measurements and prior information. The predictions are then used as
the reference inputs to DMPC. During the landing, the followers are tasked with
avoidance of USV-dependent obstacles and inter-agent collisions. In the
proposed distributed algorithm, all agents solve their local optimization
problem in parallel and we prove the convergence of the proposed algorithm.
Finally, the simulation results support the theoretical findings.
