Instrumentation, modeling, and sound metamodeling foundations for complex hybrid systems

Many of our critical infrastructures, from power grids to water distribution networks, are complex hybrid systems that use software to control their non-trivial physical dynamics. These systems must be able to capably serve their purpose, while also being reliable, dependable, safe, secure, and efficient. Representation and analysis of these features requires the creation of several distinct models. These models may encode design goals or be derived from collected instrumentation data, reflecting both how a system ought to operate and how it does operate. It is essential to ensure that all of these models consistently and accurately describe the same system. Adding or removing detail in one model may necessitate changes to several others. This work focuses on system instrumentation, modeling, and metamodeling. Our instrumentation and modeling work studies the behavior of control systems when exposed to electromagnetic disturbances. These disturbances, which may lead to data corruption, system crashes, or hardware damage, present a challenge to engineers. We develop instrumentation for monitoring systems for such disturbances, methods for analyzing the data from our instrumentation, and models of system function which can detect electromagnetic disturbances, including many that do not cause user-visible failures. Metamodeling offers a means of relating disparate models of a system, describing changes to models, and propagating those changes to other models. Our metamodeling work focuses on adding and removing detail from models -- model refinement and generalization, respectively — and on connecting models that use different formalisms -- model transformation. In order for these operations to produce meaningful results, we must ensure that they are sound; that is, they must produce models which describe, to the greatest extent possible, the same system as the models from which they are produced. We begin by creating a theory of abstract interpretation for system modeling. This theory defines a relationship between models and systems and enables verification of the soundness of our metamodeling operations. From this foundation, we create model refinement and generalization operations for specific modeling formalisms. Finally, we show how these operations can be used to perform sound model transformations”--Abstract, page iii