Control of Adaptive Systems Using a Digital Twin

While virtual reality can facilitate hands-on learning, its development faces barriers, including high costs and time demands and scalability challenges. This article presents two case studies that illustrate strategies for overcoming such barriers when training the next generation of skilled workers in environmental technologies. By examining approaches for streamlining development and increasing content relevance and scalability, we highlight lessons learned for future practice. We conclude by envisioning a future in which educational institutions can flexibly and cost-effectively prototype virtual reality in learning contexts, ensuring alignment with curricular goals and learners’ needs.

The rapid advancement of immersive technologies has created new opportunities to transform human-machine collaboration in industry. This paper presents an immersive platform with a digital twin that combines both physical and cognitive characteristics of human dynamics. By integrating multimodal sensing, human biomechanics, and cognitive state into digital twin technology, the proposed system enhances operational safety and ensures better ergonomics. The main argument is that human digital twins are not only desirable but essential for next-generation industrial systems. We discuss the limitations of existing human modeling approaches, outline the conceptual foundations of human digital twins, and demonstrate their industrial relevance across safety, productivity, ergonomics and sustainability.

In recent years, the decreasing cost and improved usability of immersive hardware and software have made extended reality (XR) increasingly attractive for industrial applications. Stand-alone systems with inside-out tracking and camera-based pass-through enable accessible mixed reality (MR) solutions. At the same time, emerging no-code software platforms allow engineers to create XR environments without programming expertise, broadening adoption across production settings. This paper explores key industrial application areas of immersive technologies through selected commercially available XR software solutions for product and process training, spatial instructions and guides, collaborative design review, and assembly and production planning.

Digital twins are frequently referred to as a promising approach for reducing greenhouse gas (GHG) emissions in industrial production; however, robust empirical evidence of their benefits under real-world conditions is largely lacking. In this case study, the emission reduction potential of a digital twin—as a conceptually described target system—is quantified ex-ante via the example of a test bench for hydraulic pumps. To this end, the GHG emissions of the original test plan for the year 2025 are determined based on actual measured energy consumption of the tested pumps and time-resolved grid electricity emission intensities. This is followed by a rule-based rescheduling, in which energy-intensive test processes are shifted to time intervals with lower emissions. The rescheduling takes operational constraints into account so that processes and equipment remain unchanged. The savings potential is determined by comparing the GHG emissions of the reference and the optimized case.