SOFIA - Multi-Platform Model Simulation of Heterogeneous Industrial Automation Systems

Industrial manufacturing systems need to be flexible and adaptable to allow continuously evolving along their life cycle, which can span several decades. In these systems, hardware and software components interact tightly to realize a production process (so-called cyber-physical production systems, CPPS). Engineers from different domains collaborate to design and implement CPPSs, including mechanical, electrical, and software engineering. Different domain-specific notations and tools are used in each subdomain. Although these languages may be syntactically similar, the semantic meaning of the language elements differs. Furthermore, even the implementation of a single language may vary among vendors of tools and hardware. Handling this heterogeneity of domain-specific languages and vendor-specific variants poses significant challenges for the development of modern, complex CPPSs.
The aim of SOFIA is to create the foundation for the platform-independent simulation of control Software for distributed systems using an adaptive execution strategy. This will allow the development of an innovative testing framework for the software used in heterogeneous, distributed control systems. Unlike state-of-the-art methods, it allows to investigate and test the effects of changing hardware configurations through simulation. Key areas investigated in this project are: An analysis of the semantic variants present in distributed control software will provide a better understanding of the language elements involved in control software development and which execution behavior is expected. Additionally, potential language variants that are not present in currently available tools, but are permitted by the specification in the standard, will be covered. A semi-formal description of implemented variability points is required that allows describing the execution strategy that is realized in a certain device. Designing a framework for adaptive model simulation forms the basis for developing an interpreter that considers the implemented variant of the device through querying the semi-formal description of the realized variant. This can lead to a best practice example for implementing execution frameworks with semantic variability.