An Approach to Reduce Network Effects in an Industrial Control and Edge Computing Scenario

Abstract

The cloud-based nature of Industry 4.0 enhances its flexibility and scalability features. To support timesensitive and mission-critical applications, whereby low latency and fast response are essential requirements, usually cloud computing resources should be placed closer to the industry. The Edge Computing concept combined with next-generation networks, such as 5G, may fulfill those requirements. This paper presents an experimental system setup that combines a Model Predictive Control approach with a compensation strategy to mitigate network delay and packet loss. The experimental system has two sides, namely, the edge and the local side. The former executes the controller and connects to the local side through a network. The latter is attached to the application and has lower computing capabilities. In our setup, the application under control is a two-wheeled mobile robot, which could act as an Automated Guided Vehicle. We defined two control objectives, the Point Stabilization, and the Trajectory Tracking, which ran through distinct network conditions, including delays and packet losses. These control objectives are only validation scenarios of the proposed approach but could be replaced by a real case path planner. The obtained results suggest that the