Cyber-physical systems (CPS) encompass the next generation of computerized control for countless aspects of the physical world and interactions thereof. The typical engineering process for CPS reuses existing designs, models, components, and software from one version to the next. For example, in automotive engineering, it is common to reuse significant portions of existing model-year vehicle designs when developing the next model-year vehicle, and such practices are common across CPS industries, from aerospace to biomedical. While reuse drastically enhances efficiency and productivity, it leads to the possibility of introducing unintended mismatches between subcomponents' specifications. For example, a 2011 US National Highway Traffic Safety Administration (NHTSA) recall of over 1.5 million model-year 2005-2010 vehicles was due to the upgrade of a physical transmission component that was not appropriately addressed in software. A mismatch between cyber and physical specifications may occur when a software or hardware upgrade (in effect, a cyber or physical specification change) is not addressed by an update (in effect, a matching specification change) in the other domain. This research will develop new techniques and software tools to detect automatically if cyber-physical specification mismatches exist, and then mitigate the effects of such mismatches at runtime, with the overall goal to yield more reliable and safer CPS upon which society increasingly depends. The detection and mitigation methods developed will be evaluated in an energy CPS testbed. While the evaluation testbed is in the energy domain, the methods are applicable to other CPS domains such as automotive, aerospace, and biomedical. The educational goals will bridge gaps between computer science and electrical engineering, preparing a diverse set of next-generation CPS engineers by developing education platforms to enhance CPS engineering design and verification skills. The proposed research is to develop new techniques and tools to automatically identify and mitigate the effects of cyber-physical specification mismatches. There are three major research objectives. The first objective is to identify cyber-physical specification mismatches. To identify mismatches, a detection problem will be formalized using the framework of hybrid input/output automata (HIOA). Offline algorithms will be designed to find candidate specifications from models and implementations using static and dynamic analyses, and then identify candidate mismatches. The second objective is to monitor and assure safe CPS upgrades. As modern CPS designs are complex, it may be infeasible to determine all specifications and mismatches between all subcomponents at design time. Runtime monitoring and verification methods will be developed for inferred specifications to detect mismatches at runtime. When they are identified, a runtime assurance framework building on supervisory control and the Simplex architecture will assure safe CPS runtime operation. The third objective is to evaluate safe CPS upgrades in an example CPS. The results of the other objectives and their ability to ensure safe CPS upgrades will be evaluated in an energy CPS testbed, namely an AC electrical distribution microgrid that interfaces DC-producing renewables like photovoltaics to AC.

Title: CPS: Breakthrough: Development of Novel Architectures for Control and Diagnosis of Safety-Critical Complex Cyber-Physical Systems This project is developing novel architectures for control and diagnosis of complex cyber-physical systems subject to stringent performance requirements in terms of safety, resilience, and adaptivity. These ever-increasing demands necessitate the use of formal model-based approaches to synthesize provably-correct feedback controllers. The intellectual merit of this research lies in a novel combination of techniques from the fields of dynamical systems, discrete event systems, reactive synthesis, and graph theory, together with new advancements in terms of abstraction techniques, computationally efficient synthesis of control and diagnosis strategies that support distributed implementations, and synthesis of acquisition of information and communication strategies. The project's broader significance and importance are demonstrated by the expected improvement of the safety, resilience, and performance of complex cyber-physical systems in critical infrastructures as well as the efficiency with which they are designed and certified. The original approach being developed is based on the combination of multi-resolution abstraction graphs for building discrete models of the underlying cyber-physical system with reactive synthesis techniques that exploit a representation of the solution space in terms of a finite structure called a decentralized bipartite transition system. The concepts of abstraction graph and decentralized bipartite transition system are novel and open new avenues of investigation with significant potential to the formal synthesis of safe, resilient, and adaptive controllers. This methodology naturally results in a set of decentralized and asynchronous controllers and diagnosers, which ensures greater resilience and adaptivity. Overall, this research will significantly impact the Science of Cyber-Physical Systems and the Engineering of Cyber-Physical Systems.

This project establishes a new framework for the formal verification of cyber-physical systems. The framework combines the power of logical decision engines and scalable numerical methods to perform safety verification of general nonlinear hybrid systems. The key difficulty with formal verification of hybrid systems is that all scalable modern verification techniques rely heavily on the use of powerful decision procedures. For hybrid systems, one needs to reason about logic formulas over the real numbers with nonlinear functions, which has been regarded as an intractable problem. The project proposes new directions for tackling the core decision problems, with the combined power of logical and numerical algorithms. The research directly leads to the development of practical tools that will push the frontier of verification of realistic cyber-physical systems to a brand new level. This project aims at fundamental research of problems that stand at the core of the design, analysis, and implementation of reliable cyber-physical systems. It combines techniques from logic, numerical analysis, and automated reasoning, and will produce a unifying methodology that is powerful to address main challenges in this field. The techniques developed in this project will significantly enhance the complexity and reliability of the next generations of cyber-physical systems. Cyber-physical systems are ubiquitous in safety-critical applications as diverse as aerospace, automotive, civil infrastructure, energy, manufacturing, and healthcare. Malfunctioning cyber-physical systems can have catastrophic economic and societal consequences. This project will have a broad range of impact in these areas. This research aims to significantly enhance the management of complexity and reliability of the next generations of cyber-physical systems, and will broadly impact all the application areas.

Until now, the "cyber" component of automobiles has consisted of control algorithms and associated software for vehicular subsystems designed to achieve one or more performance, efficiency, reliability, comfort, or safety goals, primarily based on short-term intrinsic vehicle sensor data. However, there exist many extrinsic factors that can affect the degree to which these goals can be achieved. These factors can be determined from: longer-term traces of in-built sensor data that can be abstracted as triplines, socialized versions of these that are shared amongst vehicle users, and online databases. These three sources of information collectively constitute the automotive infoverse. This project harnesses this automotive infoverse to achieve these goals through high-confidence vehicle tuning and driver feedback decisions. Specifically, the project develops software called Headlight that permits the rapid development of apps that use the infoverse to achieve one or more goals. Advisory apps can provide feedback to the driver in order to ensure better fuel efficiency, while auto-tuning goals can set car parameters to promote safety. Allowing vehicles and such apps to share vehicle data with others and to use extrinsic information results in novel information processing, assurance, and privacy challenges. The project develops methods, algorithms and models to address these challenges. Broader Impact - This project can have significant societal impact by reducing carbon emissions and improving vehicular safety, can spur innovation in tuning methods and encourage researchers to experiment with this class of cyber-physical systems. The active participation of General Motors will strongly facilitate technology transfer. There is significant outreach including high school student participation, undergraduate research activities, internships, and creation of an open framework for plug and play application developers to use.