Cooperative distributed model predictive control for embedded systems: Experiments with hovercraft formations

Abstract-- 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. Model Predictive Control (MPC) is promising for robotics, because it explicitly accounts for actuator and safety constraints, interlaces motion planning with feedback control, and is applicable to output regulation, trajectory tracking, and path following [1]. Distributed optimization and Distributed MPC (DMPC) target cyber-physical systems such as energy networks [6] on the robots is required.