This proposal will establish a framework for developing distributed Cyber-Physical Systems operating in a Networked Control Systems (NCS) environment. Specific attention is focused on an application where the computational, and communication challenges are unique due to the sheer size of the physical system, and communications between system elements include potential for significant losses and delays. An example of this is the power grid which includes large-scale deployment of distributed and networked Phasor Measurement Units (PMUs) and wind energy resources. Although, much has been done to model and analyze the impact of data dropouts and delay in NCS at a theoretical level, their impact on the behavior of cyber physical systems has received little attention. As a result much of the past research done on the `smart grid' has oversimplified the `physical' portion of the model, thereby overlooking key computational challenges lying at the heart of the dimensionality of the model and the heterogeneity in the dynamics of the grid. A clear gap has remained in understanding the implications of uncertainties in NCS (e.g. bandwidth limitations, packet dropout, packet disorientation, latency, signal loss, etc.) cross-coupled with the uncertainties in a large power grid with wind farms (e.g. variability in wind power, fault and nonlinearity, change in topology etc.) on the reliable operation of the grid. To address these challenges, this project will, for the first time, develop a modeling framework for discovering hitherto unknown interactions through co-simulation of NCS, distributed computing, and a large power grid included distributed wind generation resources. Most importantly, it addresses challenges in distributed computation through frequency domain abstractions and proposes two novel techniques in grid stabilization during packet dropout. The broader impact lies in providing deeper understanding of the impact of delays and dropouts in the Smart Grid. This will enable a better utilization of energy transmission assets and improve integration of renewable energy sources. The project will facilitate participation of women in STEM disciplines, and will include outreach with local Native American tribal community colleges This project will develop fundamental understanding of impact of network delays and drops using an approach that is applicable to a variety of CPS. It will enable transformative Wide-Areas Measurement Systems research for the smart grid through modeling adequacy studies of a representative sub-transient model of the grid along with the representation of packet drop in the communication network by a Gilbert model. Most importantly, fundamental concepts of frequency domain abstraction including balanced truncation and optimal Hankel-norm approximation are proposed to significantly reduce the burden of distributed computing. Finally, a novel `reduced copy' approach and a `modified Kalman filtering' approach are proposed to address the problem of grid stabilization using wind farm controls when packet drop is encountered.

Computer systems are increasingly coming to be relied upon to augment or replace human operators in controlling mechanical devices in contexts such as transportation systems, chemical plants, and medical devices, where safety and correctness are critical. A central problem is how to verify that such partially automated or fully autonomous cyber-physical systems (CPS) are worthy of our trust. One promising approach involves synthesis of the computer implementation codes from formal specifications, by software tools. This project contributes to this "correct-by-construction" approach, by developing scalable, automated methods for the synthesis of control protocols with provable correctness guarantees, based on insights from models of human behavior. It targets: (i) the gap between the capabilities of today's hardly autonomous, unmanned systems and the levels of capability at which they can make an impact on our use of monetary, labor, and time resources; and (ii) the lack of computational, automated, scalable tools suitable for the specification, synthesis and verification of such autonomous systems. The research is based on study of modular reinforcement learning-based models of human behavior derived through experiments designed to elicit information on how humans control complex interactive systems in dynamic environments, including automobile driving. Architectural insights and stochastic models from this study are incorporated with a specification language based on linear temporal logic, to guide the synthesis of adaptive autonomous controllers. Motion planning and other dynamic decision-making are by algorithms based on computational engines that represent the underlying physics, with provision for run-time adaptation to account for changing operational and environmental conditions. Tools implementing this methodology are validated through experimentation in a virtual testing facility in the context of autonomous driving in urban environments and multi-vehicle autonomous navigation of micro-air vehicles in dynamic environments. Education and outreach activities include involvement of undergraduate and graduate students in the research, integration of the research into courses, demonstrations for K-12 students, and recruitment of research participants from under-represented demographic groups. Data, code, and teaching materials developed by the project are disseminated publicly on the Web.