Inadequate system understanding and inadequate situational awareness have caused large-scale power outages in the past. With the increased reliance on variable energy supply sources, system understanding and situational awareness of a complex energy system become more challenging. This project leverages the power of big data analytics to directly improve system understanding and situational awareness. The research provides the methodology for detecting anomalous events in real-time, and therefore allow control centers to take appropriate control actions before minor events develop into major blackouts. The significance for the society and for the power industry is profound. Energy providers will be able to prevent large-scale power outages and reduce revenue losses, and customers will benefit from reliable energy delivery with service guarantees. Students, including women and underrepresented groups, will be trained for the future workforce in this area. The project includes four major thrusts: 1) real-time anomaly detection from measurement data; 2) real-time event diagnosis and interpretation of changes in the state of the network; 3) real-time optimal control of the power grid; 4) scientific foundations underpinning cyber-physical systems. The major outcome of this project is practical solutions to event or fault detection and diagnosis in the power grid, as well as prediction and prevention of large-scale power outages.

Part 1: Upper-limb motor impairments arise from a wide range of clinical conditions including amputations, spinal cord injury, or stroke. Addressing lost hand function, therefore, is a major focus of rehabilitation interventions; and research in robotic hands and hand exoskeletons aimed at restoring fine motor control functions gained significant speed recently. Integration of these robots with neural control mechanisms is also an ongoing research direction. We will develop prosthetic and wearable hands controlled via nested control that seamlessly blends neural control based on human brain activity and dynamic control based on sensors on robots. These Hand Augmentation using Nested Decision (HAND) systems will also provide rudimentary tactile feedback to the user. The HAND design framework will contribute to the assistive and augmentative robotics field. The resulting technology will improve the quality of life for individuals with lost limb function. The project will help train engineers skilled in addressing multidisciplinary challenges. Through outreach activities, STEM careers will be promoted at the K-12 level, individuals from underrepresented groups in engineering will be recruited to engage in this research project, which will contribute to the diversity of the STEM workforce. Part 2: The team previously introduced the concept of human-in-the-loop cyber-physical systems (HILCPS). Using the HILCPS hardware-software co-design and automatic synthesis infrastructure, we will develop prosthetic and wearable HAND systems that are robust to uncertainty in human intent inference from physiological signals. One challenge arises from the fact that the human and the cyber system jointly operate on the same physical element. Synthesis of networked real-time applications from algorithm design environments poses a framework challenge. These will be addressed by a tightly coupled optimal nested control strategy that relies on EEG-EMG-context fusion for human intent inference. Custom distributed embedded computational and robotic platforms will be built and iteratively refined. This work will enhance the HILCPS design framework, while simultaneously making novel contributions to body/brain interface technology and assistive/augmentative robot technology. Specifically we will (1) develop a theoretical EEG-EMG-context fusion framework for agile HILCPS application domains; (2) develop theory for and design novel control theoretic solutions to handle uncertainty, blend motion/force planning with high-level human intent and ambient intelligence to robustly execute daily manipulation activities; (3) further develop and refine the HILCPS domain-specific design framework to enable rapid deployment of HILCPS algorithms onto distributed embedded systems, empowering a new class of real-time algorithms that achieve distributed embedded sensing, analysis, and decision making; (4) develop new paradigms to replace, retrain or augment hand function via the prosthetic/wearable HAND by optimizing performance on a subject-by-subject basis.

Recent progress in autonomous and connected vehicle technologies coupled with Federal and State initiatives to facilitate their widespread use provide significant opportunities in enhancing mobility and safety for highway transportation. This project develops signalized intersection control strategies and other enabling sensor mechanisms for jointly optimizing vehicle trajectories and signal control by taking advantage of existing advanced technologies (connected vehicles and vehicle to infrastructure communications, sensors, autonomous vehicle technologies, etc.) Traffic signal control is a critical component of the existing transportation infrastructure and it has a significant impact on transportation system efficiency, as well as energy consumption and environmental impacts. In addition to advanced vehicle technologies, the strategies developed consider the presence of conventional vehicles in the traffic stream to facilitate transition to these new strategies in a mixed vehicle environment. The project also develops and uses simulation tools to evaluate these strategies as well as to provide tools that can be used in practice to consider the impacts of automated and connected vehicles in arterial networks. The project involves two industry partners (ISS and Econolite) to help facilitate new product development in anticipation of increased market penetration of connected and autonomous vehicles. The approach will be tested through simulation at University of Florida, through field tests at the Turner Fairbank Highway Research Center (TFHRC) and through the control algorithms that also will be deployed and tested in the field. The project will support multiple graduate students and will support creation of on-line classes. The project is at the intersection of several different disciplines (optimization, sensors, automated vehicles, transportation engineering) required to produce a real-time engineered system that depends on the seamless integration of several components: sensor functionality, connected and autonomous vehicle information communication, signal control optimization strategy, missing and erroneous information, etc. The project develops and implements optimization processes and strategies considering a seamless fusion of multiple data sources, as well as a mixed vehicle stream (autonomous, connected, and conventional vehicles) under real-world conditions of uncertain and missing data. Since trajectories for connected and conventional vehicles cannot be optimized or guaranteed, the project examines the impacts of the presence of automated vehicles on the following vehicles in a queue. The project also integrates advanced sensing technology needed to control a mixed vehicle stream, as well as address malfunctioning communications in connected and autonomous vehicles.