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.

This project designs algorithms for the integration of plug-in hybrid electric vehicles (PEVs) into the power grid. Specifically, the project will formulate and solve optimization problems critical to various entities in the PEV ecosystem -- PEV owners, commercial charging station owners, aggregators, and distribution companies -- at the distribution / retail level. Charging at both commercial charging stations and at residences will be considered, for both the case when PEVs only function as loads, and the case when they can also function as sources, equipped with vehicle-to-home (V2H) or vehicle-to-grid (V2G) energy reinjection capability. The focus of the project is on distributed decision making by various individual players to achieve analytical system-level performance guarantees. Electrification of the transportation market offers revenue growth for utility companies and automobile manufacturers, lower operational costs for consumers, and benefits to the environment. By addressing problems that will arise as PEVs impose extra load on the grid, and by solving challenges that currently impede the use of PEVs as distributed storage resources, this research will directly impact the society. The design principles gained will also be applicable to other cyber-physical infrastructural systems. A close collaboration with industrial partners will ground the research in real problems and ensure quick dissemination of results to the marketplace. A strong educational component will integrate the proposed research into the classroom to allow better training of both undergraduate and graduate students.

Production as a service (PaaS) defines a new paradigm in manufacturing that will allow designers of new products to query existing manufacturing facilities and receive information about fabrication capabilities and production availability. The access to information such as part cost, part quality, and production time will help new products to be prototyped and scaled-up quickly, while also allowing existing manufacturing facilities to benefit from underutilized equipment and labor. The PaaS framework will include both a front-end query interface for the users and a back-end analysis component. The interface will be designed to connect users with small-, mid-, and large-sized manufacturing facilities, while the scheduling and routing algorithms will provide the flexibility and security protocols needed to guarantee operational and production safety across the range of facilities. Manufacturers that utilize the PaaS framework will reap the potential of meeting customer needs in terms of cost, quality, on-time delivery, while being reactive to changing market forces. With 12 percent of the GDP represented by the manufacturing industry, the manufacturing operational improvements that will result from this EArly-concept Grant for Exploratory Research (EAGER) project have the potential to make a significant impact in the national bottom line. The aim of the PaaS platform is to enable distributed manufacturing plant locations to efficiently coordinate both within one plant location as well as across plant locations to realize a flexible service interface for supporting production management. The intellectual merit of this research lies in the extensions that will be created to the existing science and technology in service-oriented architectures to enable distributed production, while preserving proprietary information of the manufacturing systems. The key software abstraction that enables this innovation comes from the extension to the well-known APIs to capture the sophisticated query logic and diverse production requirements to meet user needs. Routing and scheduling decisions will be optimized by leveraging a global view of the current state of all of the components in the manufacturing facilities. To demonstrate scalability and ensure privacy guarantees across multiple facilities, hierarchical abstraction will be used to hide low-level details and proprietary information. The PaaS framework will transform the way manufacturing companies interact with the emerging high-value market; providing the architecture to drive innovation and enable small-, mid-, and large-scale manufacturing companies across the U.S. to compete for new product business on an even playing field.

In the recent past the term "Smart Cities" was introduced to mainly characterize the integration into our daily lives of the latest advancements in technology and information. Although there is no standardized definition of Smart Cities, what is certain is that it touches upon many different domains that affect a city's physical and social capital. Smart cities are intertwined with traffic control systems that use advanced infrastructures to mitigate congestion and improve safety. Traffic control management strategies have been largely focused on improving vehicular traffic flows on highways and freeways but arterials have not been used properly and pedestrians are mostly ignored. This work proposes to introduce a novel hierarchical adaptive controls paradigm to urban network traffic control that will adapt to changing movement and interaction behaviors from multiple entities (vehicles, public transport modes, bicyclists, and pedestrians). Such a paradigm will leverage several key ideas of cyber-physical systems to rapidly and automatically pin-point and respond to urban arterial congestion thereby improving travel time and reliability for all modes. Safety will also be improved since advanced warnings actuated by the proposed cyber-physical system will alert drivers to congested areas thereby allowing them to avoid these areas, or to adapt their driving habits. Such findings have a tangible effect on the well-being, productivity, and health of the traveling public. The primary goal is to create a Cyber-Control Network (CCN) that will integrate seamlessly across heterogeneous sensory data in order to create effective control schemes and actuation sequences. Accordingly, this project introduces a Cyber-Physical architecture that will then integrate: (i) a sub-network of heterogeneous sensors, (ii) a decision control substrate, and (iii) a sub-actuation network that carries out the decisions of the control substrate (traffic control signals, changeable message signs). This is a major departure from more prevalent centralized Supervisory Control And Data Acquisition (SCADA), in that the CCN will use a hierarchical architecture that will dynamically instantiate the sub-networks together to respond rapidly to changing cyber-physical interactions. Such an approach allows the cyber-physical system to adapt in real-time to salient traffic events occurring at different scales of time and space. The work will consequently introduce a ControlWare module to realize such dynamic sub-network reconfiguration and provide decision signal outputs to the actuation network. A secondary, complementary goal is to develop a heterogeneous sensor network to reliably and accurately monitor and identify salient arterial traffic events. Other impacts of the project include the integration of the activities with practitioners (e.g., traffic engineers), annual workshops/tutorials, and outreach to K-12 institutions.