•Calculation-driven control of physical mechanisms In cyber-physical systems (CPS), a physical mechanism is controlled or monitored by computer-based algorithms. In these systems, physical and software components are deeply intertwined; they can operate at different spatial and temporal scales, exhibit multiple and different behavioral modalities, and interact with each other in ways that vary with context. The source notes that this interweaving offers a higher degree of combination and coordination between physical and computational elements.
Mobile cyber-physical systems, as the name suggests, have the ability to move and are an important subcategory of SFS. Examples of mobile physical systems include mobile robotics and electronics carried by humans or animals. The rise in popularity of smartphones has increased interest in this area; smartphone platforms are among the most ideal mobile cyber-physical systems. Reasons for this include computing resources such as processing power and local storage; multiple sensory input/output devices such as touch screens, cameras, GPS chips, speakers, microphones, and various sensors; multiple communication mechanisms such as WiFi, 4G, EDGE, and Bluetooth; fast-developable high-level programming languages such as Java, C#, or JavaScript; and easily usable application distribution mechanisms.
In the field of industry, cyber-physical systems supported by cloud technologies have led to the development of new approaches for Industry 4.0, together with partners such as Schneider Electric, SAP, Honeywell and Microsoft, within the framework of the IMC-EZOP project of the European Commission. Cyber-physical models for future manufacturing have created a “merged-model” approach. The merged model is the digital twin of a real machine that works on a cloud platform and simulates the health status of the system by integrating both data-driven analytical algorithms and existing physical information. This model creates a digital image at the design stage; system design and physical information are logged during the process of creating a simulation model as a reference for future analysis during product design. Initial parameters can be statistically generalized, and the production process can be adjusted using test data or parameter estimation. Thanks to the connectivity provided by cloud computing technology, the merged model provides accessibility for factory managers, even when physical access to real equipment or machine data is limited.
The challenge in the development of embedded and cyber-physical systems is the great difference between the various engineering disciplines involved in design applications. From a design applications perspective, there is currently no common “language” that encompasses all the disciplines involved in SFS. Recent studies have shown that it is possible to use common simulation instead of applying new tools or design methods to integrate different disciplines; the results obtained from the MODELISAR project suggest that this approach can be implemented by proposing a new standard for co-simulation in the form of a Functional Model Interface.
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