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.

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 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.

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.

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.

This Cyber Physical Systems (CPS) project will develop a theoretical framework that facilitates safe cooperative driving in heterogeneous traffic of human-operated and autonomously-operated vehicles and demonstrate its feasibility through both simulation and physical experiments. This project will help improve the safety of a transportation system currently being transformed by vehicles with growing autonomous features.

Human-cyber-physical systems (h-CPS) are interactive engineered systems that collaborate or interact with one or more human beings to leverage the complementary strengths of both human and autonomy technologies. Medical devices, robot assistive systems, teleoperation, semi-autonomous systems, and other technology-assisted applications are all examples of h-CPS.

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

Deep Neural Networks (DNN) enabled Cyber-Physical Systems (CPS) hold great promise for revolutionizing many industries, such as drones and self-driving cars. However, the current generation of DNN cannot provide analyzable behaviors and verifiable properties that are necessary for safety assurance. This critical flaw in purely data-driven DNN sometimes leads to catastrophic consequences, such as vehicle crashes linked to self-driving and driver-assistance technologies.

Human-driven vehicles (HDVs) and automated vehicles (AVs) of all levels (Level 1-5, AVs1-5) may share the highways in the long and foreseeable future. The increasing vehicle autonomy heterogeneity and diversity may jeopardize the safe and harmonious interaction among such vehicles with mixed autonomy on highways and pose a threat to the safety of all vehicles. This may exacerbate an already growing and alarming national concern on traffic safety.