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Edge computing is more useful for real-time remote motion control of Internet of Things devices than cloud computing because of its superior low latency performance. The access system is attracting attention as a place to perform edge computing. By using the network devices of the access system as an edge computing resource, it becomes possible to realize more precise control. This paper is the first, to our knowledge, to demonstrate real-time motion control by implementing edge computing on a 10-gigabit Ethernet passive optical network based on software-defined network enabled broadband access (SEBA), which is a virtualization platform for access systems. The proposed method analyzes the effectiveness of motion control and sets the optimal network settings automatically by using SEBA to take full advantage of edge computing benefits. The proposed method can provide an optimal network for motion control without making the user aware of the network settings. We construct a general motor-based experimental system and verify the effectiveness of the proposed system. The experiment shows that it properly reflects the optimum network settings to SEBA. Also, we verify that the proposed method changed the network settings to satisfy the reference settling time of 3 s. The optimal dynamic bandwidth allocation (DBA) cycle for motion control is determined after five trials, and the settling time was reduced by 53% compared with the case where the DBA cycle was set to 20 ms and no optimization was performed.

In the remote motion control of IoT devices, the delay adversely affects control performance. In our access edge concept, in which motion control is performed from a central office, the delay becomes large due to Dynamic Bandwidth Allocation (DBA) when the Passive Optical Network (PON) is adopted as the access system. No existing motion control method considers the delay changes of the DBA algorithm. We rectify this omission by proposing a novel real-time and high-precision motion control method that links the PON device and the controller. The proposed method can improve the control performance while maintaining the bandwidth utilization efficiency without changing the DBA delay specified to meet the requirements. In the proposed method, the delay in the PON system is calculated by using a delay model based on the setting parameter received from the communication device, and is used for delay compensation control. An experiment shows that, unlike the conventional method which becomes unstable and fails to realize control, the proposed method achieves high resolution control within 4 s, which demonstrates the feasibility of motion control for factory automation using edge computing in PON systems.