Towards latency-aware control using 5G and Edge-based control architectures

Abstract: Wireless, Edge-based control and 5G networks are all examples of technologies of the emerging Industry 4.0. Understanding and evaluating these technologies is important to the development of future manufacturing and factories. However, moving from classical, wired control systems to wireless and Edge-based systems comes with new challenges such as communication delays and packet losses. The purpose of this thesis is to develop and evaluate the performance of a wireless 5G and Edge-based control system. Firstly, we aim to find the achievable end-to-end latency of three different network architectures: local control, control over wired Ethernet and control over wireless 5G. Secondly, we propose and test a conservative tuning approach on a Ball and Beam process which represents a time-sensitive and mission-critical process. The proposed conservative tuning approach is based on an Internal Model Control framework which enables an adjustment of the controller parameters based onthe worst-case measured latency. The results show that the measured latency increases as the Task interval time is increasing and as the controller is moving further away from a local level. The results also show that the introduced latency over 5G is making the system unstable if the latency is not taken into account in the design. The proposed conservative tuning approach successfully adjusts the parameters to remove this unstable behavior but degrades the control performance and shows signs of an overly conservative tuning compared to a local controller. The thesis concludes that the proposed conservative tuning approach shows promising results but would benefit from being further developed towards a latency-aware controller. This could be achieved by firstly improving the way latency is measured to enable extensive data collection. The data could then be utilized by using machine learning or time-series to predict the latency and adjust the parameters in real-time, using the proposed tuning approach.