The rapid development in the field of Unmanned Aerial Vehicles (UAVs) is driven by new applications in agriculture, logistics, inspection and smart manufacturing. The future keys in these domains are the abilities to autonomously interact with the environment and with other robotic systems. For this reason, this work is providing control engineering solutions to contribute to these key capabilities. The underlying challenges are hereby represented by a cooperative flying manipulation system with a sensor- and a manipulator-UAV.In the first part of this project, dynamic and kinematic models have been developed to describe a UAV’s motion. The presented models have been subsequently used to develop a nonlinear model predictive control (NMPC) strategy. For this purpose, the performance of several NMPC solvers and constraint handling techniques has been evaluated. The resulting NMPC control has been validated with real AR.Drone 2.0 and DJI M100 quadrotors. This includes collision avoidance and advanced tracking scenarios. In the subsequent stage of this project, the developed NMPC has been extended for UAVs with an attached robotic arm. The resulting control approach has been implemented and validated in a real bottle grasping scenario.The final step of this work has been the NMPC of multiple cooperating UAVs. The computational complexity of such scenarios conflicts directly with the fast UAV dynamics. In addition, control objectives and system topologies can dynamically change. To address these challenges, the DENMPC software framework has been developed. DENMPC provides a computationally efficient central NMPC strategy that allows changing the control scenario at runtime. This has been finally stated in the control of a full cooperative aerial manipulation scenario with a real sensor- and manipulator-UAV.