This paper presents experiments for embedded cooperative distributed model predictive control applied to a team of hovercraft floating on an air hockey table. The hovercraft collectively solve a centralized optimal control problem in each sampling step via a stabilizing decentralized real-time iteration scheme using the alternating direction method of multipliers. The efficient implementation does not require a central coordinator, executes onboard the hovercraft, and facilitates sampling intervals in the millisecond range. The formation control experiments showcase the flexibility of the approach on scenarios with point-to-point transitions, trajectory tracking, collision avoidance, and moving obstacles.

Recent advances in Model Predictive Control (MPC) algorithms and methodologies, combined with the surge of computational power of available embedded platforms, allows the use of real-time optimization-based control of fast mechatronic systems. This paper presents an implementation of an optimal guidance, navigation and control (GNC) system for the motion control of a small-scale electric prototype of a thrust-vectored rocket. The aim of this prototype is to provide an inexpensive platform to explore GNC algorithms for automatic landing of sounding rockets. The guidance and trajectory tracking are formulated as continuous-time optimal control problems and are solved in real-time on embedded hardware using the PolyMPC library. An Extended Kalman Filter (EKF) is designed to estimate external disturbances and actuators offsets. Finally, indoor and outdoor flight experiments are performed to validate the architecture.