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.

The OPC UA communication architecture is currently becoming an integral part of industrial automation systems, which control complex production processes, such as electric power generation or paper production. With a recently released extension for pub/sub communication, OPC UA can now also support fast cyclic control applications, but the bottlenecks of OPC UA implementations and their scalability on resource-constrained industrial devices are not yet well understood. Former OPC UA performance evaluations mainly concerned client/server round-trip times or focused on jitter, but did not explore resource bottlenecks or create predictive performance models. We have carried out extensive performance measurements with OPC UA client/server and pub/sub communication and created a CPU utilization prediction model based on linear regression that can be used to size hardware environments. We found that the server CPU is the main bottleneck for OPC UA pub/sub communication, but allows a throughput of up to 40,000 signals per second on a Raspberry Pi Zero. We also found that the client/server session management overhead can severely impact performance, if more than 20 clients access a single server.