Many cyber-physical systems (CPS) have real-time (RT) requirements. For these RT-CPS, such as a network of unmanned aerial vehicles that deliver packages to customers? homes or a robot that performs/aides in cardiac surgery, deadline misses may result in economic losses or even fatal consequences. At the same time, as these RT-CPS interact with, and are depended on by, humans, they must also be trustworthy. The goal of this research is to design secure RT-CPS that are less complex, easier to analyze, and reliable for critical application domains such as defense, medicine, transportation, manufacturing, and agriculture, to name just a few. Since RT-CPS now permeate most aspects of our daily lives, especially in the smart city and internet-of-things (IoT) context, this research will improve confidence in automated systems by users. Research results will be disseminated to both academia and industry, and permit timely adoption since the hardware required in this research is already publicly available. This project will result in a pipeline of engineers and computer scientists who are well-versed in the interdisciplinary nature of securing RT-CPS, as well as course modules and red-teaming exercises for undergraduate students in all engineering disciplines and interactive learning modules and internship experience for K-12 students in D.C., Detroit, Dallas, and St. Louis.

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).

Autonomous systems are subject to multiple regulatory requirements due to their safety-critical nature. In general, it is infeasible to guarantee the satisfaction of all requirements under all conditions. In such situations, the system needs to decide how to prioritize among them. Two main factors complicate this decision. First, the priorities among the conflicting requirements may not be fully established. Second, the decision needs to be made under uncertainties arising from both the learning-based components within the system and the unstructured, unpredictable, and non-cooperating nature of the environments. Therefore, establishing the correctness of autonomous systems requires specification languages that capture the unequal importance of the requirements, quantify the violation of each requirement, and incorporate uncertainties faced by the systems.

As autonomous systems start to operate in open, uncontrolled environments alongside humans, safety becomes a major concern. In applications in which human-operated systems and autonomous systems are in close interaction, the heterogeneity causes different agents to exhibit different behaviors under the same situation due to the differences in how they see the world and make decisions. For example, autonomous vehicles tend to be more conservative than average human drivers, leading to instances of confusion and frustration of human drivers when encountering an autonomous vehicle. As a result, understanding the effects of inconsistencies among interacting agents on the overall system is critical for the adoption and acceptance of autonomous systems.

As autonomous systems start to operate in open, uncontrolled environments alongside humans, safety becomes a major concern. In applications in which human-operated systems and autonomous systems are in close interaction, the heterogeneity causes different agents to exhibit different behaviors under the same situation due to the differences in how they see the world and make decisions. For example, autonomous vehicles tend to be more conservative than average human drivers, leading to instances of confusion and frustration of human drivers when encountering an autonomous vehicle. As a result, understanding the effects of inconsistencies among interacting agents on the overall system is critical for the adoption and acceptance of autonomous systems.

This project envisions mobile cyber-physical systems (CPS) where people carrying cell phones generate large amounts of location information that is used to sense, compute and monitor human interactions with the physical environment during environmental dislocations. The main objective will be to identify the types of reactions populations have to a given type of shock, providing decision makers with accurate and informative data-driven representations they can use to create preparedness and response plans. Additionally, the outcomes of this project will allow for the development of tools to assess and improve the effectiveness of different types of preparedness and response policies through feedback loops in the mobile CPS. These feedback loops could show how community behaviors during shocks change when policies are re-defined based on the computations of the CPS, and vice-versa. Previous work by the PI and others has already showed that CPS integrating people and cell phones as sensing platforms can be used to collect location information at large scale and to compute, using data mining and machine learning techniques, human mobility behaviors during shocks. However, most of the results are very limited and ad-hoc, lacking any type of serious applicability from a preparedness and response policy. This project will advance the state of the art by developing accurate methods and effective tools for decision-making during shocks in mobile CPS. From a broader impacts perspective, the proposed research will contribute in two areas: (a) real-world deployments, to promote data-driven policy development, data-driven analyses of human behavior, and the use of feedback loops in mobile CPS for decision-making assessment; and (b) the creation of an educational plan and training opportunities in the areas of data science for social good and mobile CPS for decision making.

The overarching goal of the proposed research is to derive critical information and characterization of large scale generic nonlinear dynamical systems using limited observables. In the present state-of-the-art in data-driven dynamical system analysis, all the underlying state measurements and the time evolution of these states are required. Access to all of the dynamical states measurements in real-world is impossible or expensive. The objective of the proposal is to develop data-driven tools for dynamic system identification, classification and root-cause analysis of dynamic events, and prediction of system evolution. The research team will specifically conduct research on using available measurements to perform near real-time applications for various dynamic events that occur in electric power systems. The data analytics proposed are applicable to general non-linear dynamic systems and can be easily applied to other cyberphysical systems (CPS). More broadly, there is a large effort in the CPS and control community to model real world systems that we all interact with on a daily basis (such as transportation systems, communication networks, world wide web, etc.) as dynamical systems and thus, the theory and techniques developed through this project will enable online monitoring of these critical systems, allowing operators to quickly analyze these systems for any unstable/anomalous behavior from minimal data streams. The project will promote various educational and outreach activities including developing new courses, short courses, activities in schools, and scholarships for women and underrepresented minority students.

Active user participation in large-scale infrastructure systems, while presenting unprecedented opportunities, also poses significant challenges for the operator. One such example is electric power distribution systems, where the massive integration of distributed energy resources (DERs) and flexible loads motivates new decision-making paradigms via demand response through user engagement. This project introduces a novel approach for intelligent decision making in power distribution systems to efficiently leverage flexible demand commitments in highly uncertain and stochastic environments. The project goals are to (1) develop analytics required to enable actionable demand-side flexibility from several small consumers by adequately representing their constraints regarding electricity usage and their interactions with the system and the energy provider; and (2) develop a prototype for demand-side coordination using an open-source testbed for distribution systems management and evaluate the proposed algorithms with real-world utility data. Successful completion of this project will provide solutions to adaptive and smart infrastructure systems in which passive users turn into active participants. For the demand response focus here, this project will enable high levels of penetration of flexible loads and DERs economically through the transformation of grid operation from load following to supply following. The results from this project will provide valuable guidance to policymakers and electric utilities in managing aggregator-driven markets.

The availability of vast amounts of operational and end-user data in cyber-physical systems implies that paradigm improvements in monitoring and control can be attained via learning by many artificial intelligence agents despite them possessing vastly different abilities. Engaging this heterogeneous agent base in the context of the smart grid requires the use of hierarchical markets, wherein end-users participate in downstream markets collectively through aggregators, who in turn are coordinated by an upstream market. The goal of this project is to conduct a systematic study of such market-mediated learning and control. This project aims at much deeper levels of participation from end-users contributing electricity generation such as rooftop solar, shedding load via demand response, and providing storage capabilities such as electric vehicle batteries, to transform into reliable distributed energy resources (DER) at the level of wholesale markets. A methodological theme is multi-agent reinforcement learning (MARL) by agents that control physical systems via actions at different levels of the hierarchy. Underlying the whole project are well-founded physical models of the transmission and distribution grids, which provide structure to the problem domain and concrete use cases. This project facilitates a deeper level of decarbonization in the electricity sector, and contributes to climate change solutions by engineering a flat, interactive grid architecture that allows significant DERs to provide electricity services to both local and regional grids. Engagement with a grid-level market operator enables the project to address a problem space of immediate relevance to the current electricity grid. The project also includes the development of educational materials on data-analytics and energy systems. Intrinsic to the program are efforts at outreach to involve high-school students via demonstrations and lectures based on the technology developed.

This Cyber-Physical Systems (CPS) project will make foundational methodological advances that enable safe and robust reinforcement learning (RL)-based control algorithmic solutions that are driven by problems in smart traffic signal control systems. Recent advances in computation, communication, storage, and sensing have led to a demand for data-driven learning-based decision-making and control in modern cyber-physical systems (CPSs), such as smart transportation systems. In such systems, decision-making agents need to operate safely and in a robust manner while working in complex environments with constraints that need to be respected. This project will develop foundational advances in robust RL solutions, and safe and constrained RL with provable guarantees by taking traffic signal control systems within smart transportation systems as our motivating CPS application and evaluation platform. This work will additionally focus on advancing curriculum development, recruitment of students from under-represented groups, involvement of undergraduate students in research, K-12 outreach, and also research community outreach via workshops, conference sessions, and seminars. The researchers will interface with companies and other stakeholders to communicate the results of the research as well as provide them with educational material on methodology.

The project's impact is the engineering of more reliable, safer, and better performing next generation Cyber-Physical Systems (CPS) arising in domains involving automotive vehicles, medical devices, avionics, power grids, and autonomous agents. As these systems operate in environments that cannot always be perfectly anticipated/modeled, it is crucial that robustness be a prime objective right from the design phase. The project's novelty is the development of scalable and rigorous formal algorithmic approaches for facilitating such robust CPS engineering. These approaches advance classical discrete algorithmic techniques for use in quantitative environments in which CPS operates. The research aspect of the project is complemented with the creation of graduate and undergraduate courses teaching core logical and algorithmic analyses skills to scientists and engineers about to enter the workforce.