This project explores balancing performance considerations and power consumption in cyber-physical systems, through algorithms that switch among different modes of operation (e.g., low-power/high-power, on/off, or mobile/static) in response to environmental conditions. The main theoretical contribution is a computational, hybrid optimal control framework that is connected to a number of relevant target applications where physical modeling, control design, and software architectures all constitute important components. The fundamental research in this program advances state-of-the-art along four different dimensions, namely (1) real-time, hybrid optimal control algorithms for power management, (2) power-management in mobile sensor networks, (3) distributed power-aware architectures for infrastructure management, and (4) power-management in embedded multi-core processors. The expected outcome, which is to enable low-power devices to be deployed in a more effective manner, has implications on a number of application domains, including distributed sensor and communication networks, and intelligent and efficient buildings. The team represents both a research university (Georgia Institute of Technology) and an undergraduate teaching university (York College of Pennsylvania) in order to ensure that the educational components are far-reaching and cut across traditional educational boundaries. The project involves novel, inductive-based learning modules, where graduate students team with undergraduate researchers.

The project investigates a formal verification framework for artificial pancreas (AP) controllers that automate the delivery of insulin to patients with type-1 diabetes (T1D). AP controllers are safety critical: excessive insulin delivery can lead to serious, potentially fatal, consequences. The verification framework under development allows designers of AP controllers to check that their control algorithms will operate safely and reliably against large disturbances that include patient meals, physical activities, and sensor anomalies including noise, delays, and sensor attenuation. The intellectual merits of the project lie in the development of state-of-the-art formal verification tools, that reason over mathematical models of the closed-loop including external disturbances and insulin-glucose response. These tools perform an exhaustive exploration of the closed loop system behaviors, generating potentially adverse situations for the control algorithm under verification. In addition, automatic techniques are being investigated to help AP designers improve the control algorithm by tuning controller parameters to eliminate harmful behaviors and optimize performance. The broader significance and importance of the project are to minimize the manual testing effort for AP controllers, integrate formal tools in the certification process, and ultimately ensure the availability of safe and reliable devices to patients with type-1 diabetes. The framework is made available to researchers who are developing AP controllers to help them verify and iteratively improve their designs. The team is integrating the research into the educational mission by designing hands-on courses to train undergraduate students in the science of Cyber-Physical Systems (CPS) using the design of AP controllers as a motivating example. Furthermore, educational material that explains the basic ideas, current challenges and promises of the AP concept is being made available to a wide audience that includes patients with T1D, their families, interested students, and researchers. The research is being carried out collaboratively by teams of experts in formal verification for Cyber-Physical Systems, control system experts with experience designing AP controllers, mathematical modeling experts, and clinical experts who have clinically evaluated AP controllers. To enable the construction of the verification framework from the current state-of-the-art verification tools, the project is addressing major research challenges, including (a) building plausible mathematical models of disturbances from available clinical datasets characterizing human meals, activity patterns, and continuous glucose sensor anomalies. The resulting models are integrated in a formal verification framework; (b) simplifying existing models of insulin glucose response using smaller but more complex delay differential models; (c) automating the process of abstracting the controller implementation for the purposes of verification; (d) producing verification results that can be interpreted by control engineers and clinical researchers without necessarily understanding formal verification techniques; and (e) partially automating the process of design improvements to potentially eliminate severe faults and improve performance. The framework is evaluated on a set of promising AP controller designs that are currently under various stages of clinical evaluation.

Cyber physical systems (CPSs) are merging into major mobile systems of our society, such as public transportation, supply chains, and taxi networks. Past researchers have accumulated significant knowledge for designing cyber physical systems, such as for military surveillance, infrastructure protection, scientific exploration, and smart environments, but primarily in relatively stationary settings, i.e., where spatial and mobility diversity is limited. Differently, mobile CPSs interact with phenomena of interest at different locations and environments, and where the context information (e.g., network availability and connectivity) about these physical locations might not be available. This unique feature calls for new solutions to seamlessly integrate mobile computing with the physical world, including dynamic access to multiple wireless technologies. The required solutions are addressed by (i) creating a network control architecture based on novel predictive hierarchical control and that accounts for characteristics of wireless communication, (ii) developing formal network control models based on in-situ network system identification and cross-layer optimization, and (iii) designing and implementing a reference implementation on a small scale wireless and vehicular test-bed based on law enforcement vehicles. The results can improve all mobile transportation systems such as future taxi control and dispatch systems. In this application advantages are: (i) reducing time for drivers to find customers; (ii) reducing time for passengers to wait; (iii) avoiding and preventing traffic congestion; (iv) reducing gas consumption and operating cost; (v) improving driver and vehicle safety, and (vi) enforcing municipal regulation. Class modules developed on mobile computing and CPS will be used at the four participating Universities and then be made available via the Web.

Water is a critical resource and a lifeline service to communities worldwide; the generation, treatment, distribution and maintenance of water workflows is typically managed by local governments and water districts. Recent events such as water supply disruptions caused by Hurricane Sandy in 2012 and the looming California drought crisis clearly indicate society's dependence on critical lifeline services such as water and the far-reaching impacts that its disruption can cause. Over the years, these critical infrastructures have become more complex and often more vulnerable to failures. The ability to view water workflows as a community wide cyber-physical system (CPS) with multiple levels of observation/control and diverse players (suppliers, distributors, consumers) presents new possibilities. Designing robust water systems involves a clear understanding of the structure, components and operation of this CPS system and how community infrastructure dynamics (e.g. varying demands, small/large disruptions) impact lifeline service availabilities and how service level decisions impact infrastructure control. The proposal emphasizes a new approach to exploring engineering systems that will result in substantial advances in the understanding of lifeline systems and approaches to make them adaptive and resilient. Building resilience into urban lifelines raises a number of monumental challenges including identifying the aspects of systems that can be observed/sensed and adapted and to developing general principles that can guide adaptation. The key idea is to develop methodologies to understand operational performance and resilience issues for real-world community water infrastructures and explore solutions to problems in cyberspace before instantiating them into a physical infrastructure. The effort includes: 1) Developing a flexible modeling framework that captures system needs at multiple levels of temporal and spatial abstraction; 2) Developing real-time algorithms supporting resilience; 3) Designing adaptations for water systems using a data-driven approach; and 4) Demonstrating the important broader impact of the research on critical water system infrastructure at the Global City Technology Challenge and the longer term impact on infrastructure for a resilient control framework.

Water is a critical resource and a lifeline service to communities worldwide; the generation, treatment, distribution and maintenance of water workflows is typically managed by local governments and water districts. Recent events such as water supply disruptions caused by Hurricane Sandy in 2012 and the looming California drought crisis clearly indicate society's dependence on critical lifeline services such as water and the far-reaching impacts that its disruption can cause. Over the years, these critical infrastructures have become more complex and often more vulnerable to failures. The ability to view water workflows as a community wide cyber-physical system (CPS) with multiple levels of observation/control and diverse players (suppliers, distributors, consumers) presents new possibilities. Designing robust water systems involves a clear understanding of the structure, components and operation of this CPS system and how community infrastructure dynamics (e.g. varying demands, small/large disruptions) impact lifeline service availabilities and how service level decisions impact infrastructure control. The proposal emphasizes a new approach to exploring engineering systems that will result in substantial advances in the understanding of lifeline systems and approaches to make them adaptive and resilient. Building resilience into urban lifelines raises a number of monumental challenges including identifying the aspects of systems that can be observed/sensed and adapted and to developing general principles that can guide adaptation. The key idea is to develop methodologies to understand operational performance and resilience issues for real-world community water infrastructures and explore solutions to problems in cyberspace before instantiating them into a physical infrastructure. The effort includes: 1) Developing a flexible modeling framework that captures system needs at multiple levels of temporal and spatial abstraction; 2) Developing real-time algorithms supporting resilience; 3) Designing adaptations for water systems using a data-driven approach; and 4) Demonstrating the important broader impact of the research on critical water system infrastructure at the Global City Technology Challenge and the longer term impact on infrastructure for a resilient control framework.

Large-scale applications of cyber-physical systems (CPS) such as commercial buildings with Building Automation System (BAS)-based demand response (DR) can play a key role in alleviating demand peaks and associated grid stress, increased electricity unit cost, and carbon emissions. However, benefits of BAS alone are often limited because their demand peak reduction cannot be maintained long enough without unduly affecting occupant comfort. This project seeks to develop control algorithms to closely integrate battery storage-based DR with existing BAS capabilities. The overarching objective is to expand the building's DR capabilities, providing crucial benefits towards smarter grids, while maintaining appropriate occupant comfort and reducing building ownership cost. This project follows a 2-phase approach towards more effective integration. First, building peak demand forecasting will be added to existing battery dispatch methods. Under electricity tariffs geared towards daily [monthly] peaks, such forecasting could result in the same battery-enabled demand charge (dollars per kW) savings as previously demonstrated storage dispatch algorithms. However, supply charges (dollars per kWh) and associated emissions would be reduced because battery dispatch would be geared towards reducing only the biggest daily [monthly] peaks while not incurring roundtrip charging losses on more moderate peaks. Phase 2 builds on phase 1, adding closer integration and systematic optimization to the algorithms for forecasting, BAS, and battery dispatch. This integration will allow the integrated CPS to manipulate the BAS process itself, thereby optimizing, e.g., light dimming, temperature set-points, and pre-cooling in unison with battery-based DR. Feasibility and future promise of such experimental control methodology will be measured by a multi-objective cost function which includes demand and energy charges, savings from DR participation, storage equipment capital expenditure (required size, achievable lifetime), and occupant comfort. Integrating BAS- with battery-based DR is nascent, mostly because the peak demand forecast, BAS, and storage dispatch algorithms that such a CPS requires have yet to be developed. This project seeks to lay important methodological groundwork for such applications, thus furthering commercial buildings' role in the Internet of Things. The PI's participation in the NIST/US-Ignite Global City Team Challenge (with partners Urban Electric Power, Siemens Corporate Technology, City University of New York, and NY-Best) furthers public engagement with such technology and will help catalyze its translation into the commercial space.

Human-in-the-loop control strategies in which the user performs a task better, and feels more confident to do so, is an important area of research for cyber-physical systems. Humans are very adept at learning to control complex systems, particularly those with non-intuitive kinematic constraints (e.g., cars, bicycles, wheelchairs, steerable needles). With the advent of cyber-physical systems, (physical systems integrated with cyber control layer), human control is no longer constrained to system inputs. Users can also control system outputs through a number of different teleoperation mappings. Given all this flexibility, what is the most intuitive way for a human user to control an arbitrary system and how is intuitiveness quantified? The project focuses on human-in-the-loop control for medical needles, which steer with bicycle-like kinematics. These needles could be used in a variety of medical interventions including tissue biopsy, tumor ablation, abscess drainage, and local drug delivery. We have explored a variety of teleoperation mappings for human control of these steerable needles; yet, we have found inconsistencies between objective performance metrics (e.g., task time and error), and post-experimental surveys on comfort or ease-of use. Users occasionally report a preference for control mappings, which objectively degrade performance, and vice versa. It is important to measure the real-time engagement of the user with the physical system in order to capture the nuances of how different control mappings affect physical effort, mental workload, distraction, drowsiness, and emotional response. Physiological sensors such as electroencephalography (EEG), galvanic skin response (GSR), and electromyography (EMG), can be used to provide these real-time measurements and to quantitatively classify the intuitiveness of new teleoperation algorithms. Broader Impacts: Intuitive and natural human-in-the-loop control interfaces will improve human health and well being, through applications in surgery and rehabilitation. The results of this study will be disseminated publicly on the investigator's laboratory website, a conference workshop, and a new medical robotics seminar to be held jointly between UT Dallas and UT Southwestern Medical Center. Outreach activities, lab tours, and mentoring of underrepresented students at all levels, will broaden participation in STEM. Additionally, the proximity of the investigator?s hospital-based lab to medical professionals will engage non-engineers in design and innovation

It is now recognized that cloud data centers are a significant consumer of energy resources and a substantial source of greenhouse gas emission. On the other hand, the intermittency and uncertainty of renewable energy present a daunting operating challenge for the electricity grid. The key idea behind this CRII: Cyber-Physical Systems project is that these two challenges are in fact symbiotic: data centers can be virtual batteries for the electricity grid. Specifically, data centers are large loads, but are also flexible. If the electricity grid can call on the flexibilities of data centers via appropriate demand response programs, this will be a crucial tool for easing the incorporation of renewable energy into the electricity grid. Unfortunately, despite the great potential, the current reality is that data centers perform little demand response. This project aims at the interdisciplinary challenges of enabling demand response from cloud data centers to realize the societal benefits. The overarching goal of this project is to develop an intellectual framework to understand and guide the realization of demand response from cloud data centers, to address engineering and economic challenges in order to manage the daunting risk. This project will first quantify the potential economic and environmental benefits of demand response from cloud data centers. The quantification includes the societal cost savings and emission reductions from networked data centers through geographical load balancing, and the impacts of demand response taxonomy. Built upon the first thrust, this project will continue to tackle the interdisciplinary challenges of both local control algorithm design and global market design for data center demand response in order to facilitate their participation in various demand response programs. The researchers will study prediction-based pricing design and analysis, demand response program design based on optimization decomposition, and distributed online algorithm design for risk management and distributed control. The results of this project will, at the societal level, help utility companies and load serving entities realize the great potential that lies in the Cloud, and, furthermore, design demand response programs that provide right incentives for data center operators to participate. At a local level, this project will help guide the management of geographically distributed data centers in participating in the right demand response programs. The control algorithms and demand response programs, as well as the methodology, can be applied beyond data centers. This research will create new knowledge in distributed online algorithm design and optimization-based market design. Additionally, this project will help design an interdisciplinary course Sustainable IT and IT for Sustainability. Personnel involved in this project, graduates and undergraduates, will receive innovation experiences through the algorithm design, analysis, implementation, and testing.

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.

This cross-disciplinary research proposes a patient-specific cost-saving approach to the design and optimization of healthcare cyber-physical systems (HCPS). The HCPS computes the patient's physiological state based on sensors, communicates this information via a network from home to hospital for quantifying risk indices, signals the need for critical medical intervention in real time, and controls vital health signals (e.g., cardiac rhythm, blood glucose). The research proposed under the HCPS paradigm will treat the human body as a complex system. It will entail the development of mathematical models that capture the time-dependence and fractal behavior of physiological processes and the design of quality-of-life (QoL) control strategies for medical devices. The research will advance the understanding of the correlations between physiological processes, drug treatment, stress level and lifestyle. To date, the complex interdependence, variability and individual characteristics of physiological processes have not been taken into account in the design of medical devices and artificial organs. The existing mathematical approaches rely on reductionist and Markovian assumptions. This research project will rethink the theoretical foundations for the design of healthcare cyber-physical systems by capturing the interdependencies and fractal characteristics of physiological processes within a highly dynamic network. To establish the theoretical foundations of HCPS, a three-step approach will be followed: (i) construct a multi-scale non-equilibrium statistical physics inspired framework for patient modeling that captures the time dependence, non-Gaussian behavior, interdependencies and multi-fractal behavior of physiological processes; (ii) develop adaptive patient-specific and physiology-aware (multi-fractal) close-loop control algorithms for dynamic complex networks; (iii) design algorithms and methodologies for the HCPS networked components that account for biological and technological constraints. This research will significantly contribute to early chronic disease detection and treatment. Models and implementable algorithms, which can both predict physiological dynamics and assess the risk of acute and chronic diseases, will be valuable instruments for patient-centered healthcare. This in-depth mathematical analysis of physiological complexity facilitates a transformative multimodal and multi-scale approach to CPS design with healthcare applications. The project not only addresses the current scientific and technological gap in CPS, but can also foster new research directions in related fields such as the study of interdependent networks with implications for understanding homeostasis and diseases and the study and control of complex systems. The cyber-physical systems designed under this newly proposed paradigm will have vital social and economic implications, including the improvement of QoL and the reduction of lost productivity rates due to chronic diseases. The project will offer interdisciplinary training for graduate, undergraduate and K-12 students. The PI will integrate the research results within his courses at University of Southern California and make them widely available through the project website. Moreover, the PI will enhance civic engagement by involving college and K-12 students in community outreach activities that will raise awareness of the important role of health monitoring.