Ensuring consistency in scalable-detail models for DT-based control

Digital Twins (DTs for short) are a powerful aid for creating, assessing and maintaining control strategies. This use of DTs however requires that the physical entities to control be described at different levels of detail. For example, simple I/O models are used to compute parameters of modulating controllers, more time-accurate ones may be required to set up and assess logic controls, high-accuracy, possibly nonlinear ones may serve for overall strategy verification, and for software-in-the-loop testing, also the host computing/network architecture needs representing. In such a complex scenario, guaranteeing that all the descriptions of all elements are consistent with one another is a relevant problem. We discuss this matter and propose a solution, in the form of a modelling paradigm where - as a novel contributions - relationships (in a sense analogous to what the term means in database theory) can be instated and enforced. This allows to create and maintain knowledge based made of interrelate data and models, embracing all the major DT interpretations proposed so far in the literature, or said more explicitly, combining data-driven and model-driven DTs in a single framework. We also provide an illustrative example.