Cyber–physical systems are created at the intersection of physical processes, networking, and computation. For applications developed to implement cyber–physical interactions, in the face of limited resources, an optimization of efficiency needs to be handled across all entities—communication, computing, and control. This gives rise to the emerging area of “co-design” that addresses the challenge of designing applications or systems at the intersection of control, communication, and compute when these domains can and should no longer be considered to be fully independent. In this article a co-design framework is presented that provides a structured way of addressing the co-design problem. Different co-design degrees are specified that group application design approaches according to their needs on criticality/dependability and relate these to the knowledge, insights, and required interactions with the communication and computation infrastructure. The applicability of the framework is illustrated for the example of autonomous mobile robots, showing how different co-design degrees exploit the relationships and permit the identification of technical solutions that achieve improved resource efficiency, increased robustness, and improved performance compared to traditional application design approaches. The framework is of relevance both for concrete near-term application implementation design as well as more futuristic concept development.

In this paper we propose and quantitatively evaluate three performance optimization methods that exploit the concept of communication-compute-control co-design by introducing awareness of communication and compute characteristics into the application logic in different ways to improve overall system performance. We have implemented a closed-loop control of a robotic arm over a wireless network where the controller is deployed into an edge cloud environment. When implementing an industrial system that leverages network and cloud technologies, the level of determinism of the control application can be decreased by nature. This means that some imperfections may be introduced into the control system, and the closed-loop control in substance changes to open-loop during disturbances. We aim to improve the performance of these open-loop control periods by applying methods that can compensate for the imperfections statistically or in a guaranteed way. We demonstrate that co-design-based application improvements with minimal dependencies on the underlying technologies can already yield an order of magnitude gain when it comes to the accurate execution of the robot trajectories during the openloop control periods. Furthermore, by combining the proposed methods, the performance improvements add up and can produce up to 45% shorter trajectory executions compared to individual evaluations.