This paper presents the development of a networked control system for maritime applications within the joint project GALILEOnautic to enable autonomous shipping and optimal maneuvering in harbor environments. To achieve this objective, all networked vehicles estimate position, velocity, orientation and time dynamically using a maritime navigation filter. These vehicle states are broadcasted to a central computation unit via long term evolution (LTE) mobile communication in combination with virtual private network (VPN). After a data synchronization step, an optimization algorithm uses the vessel states to calculate model-based optimal trajectories for each networked participant. These trajectories consist of target positions at certain instants of time and optimal settings for the rudder and propulsion units which are used as feedforward variables for a local trajectory controller on each vessel. To compensate smaller errors due to model inaccuracies during trajectory calculation, a feedback control loop is also established. Beside active network participants, other vehicles can be considered as well using data which is broadcasted by the automatic identification system (AIS). This paper presents the promising results of GALILEOnautic demonstrating an encounter situation of two networked vessels and one disturbing, non-networked participant. To demonstrate how the approach is able to avoid critical situations, three different demonstrators with increasing complexity are shown: networked robots, unmanned surface vehicles, and a ship-handling simulator.