  In this article the implementation of a controller and specifically of a
Model Predictive Controller (MPC) on an Edge Computing device, for controlling
the trajectory of an Unmanned Aerial Vehicle (UAV) model, is examined. MPC
requires more computation power in comparison to other controllers, such as PID
or LQR, since it use cost functions, optimization methods and iteratively
predicts the output of the system and the control commands for some determined
steps in the future (prediction horizon). Thus, the computation power required
depends on the prediction horizon, the complexity of the cost functions and the
optimization. The more steps determined for the horizon the more efficient the
controller can be, but also more computation power is required. Since sometimes
robots are not capable of managing all the computing process locally, it is
important to offload some of the computing process from the robot to the cloud.
But then some disadvantages may occur, such as latency and safety issues. Cloud
computing may offer "infinity" computation power but the whole system suffers
in latency. A solution to this is the use of Edge Computing, which will reduce
time delays since the Edge device is much closer to the source of data.
Moreover, by using the Edge we can offload the demanding controller from the
UAV and set a longer prediction horizon and try to get a more efficient
controller.
