Human-robot teams engaged in transportation and data collection will often share a common physical workspace. This project will investigate fundamental challenges in human-cyberphysical-systems (h-CPS) for cooperative aerial payload transport. First, Unmanned Aerial Vehicles (UAVs) cooperatively lift and carry a payload through a cluttered environment under uncertain winds. The multi-UAV system (MUS) functions autonomously to allow human companions to focus attention on their environment while interacting with the MUS. We propose a novel interface where an operator pushes on the slung payload to guide the team and coordinates the mission through a networked tablet. A novel cooperative control strategy safely guides the MUS while physics-based algorithms distinguish human inputs from environmental disturbances. Flight tests will demonstrate and validate the h-CPS. The PI and mentored postdoctoral researcher will involve students from under-represented groups and K-12 students in safe MUS flight demonstrations. This project offers three research advances: MUS scalability and collision avoidance guarantees through continuum deformation cooperative control, safe MUS compensation for vehicle anomalies, and cognitively-tractable user interfaces. Particularly novel to this work is the h-CPS interface in which an operator pushes on the payload to guide the MUS team. We will apply linear momentum analysis to sense haptic cues and will validate our models in simulation and flight testing. Mission-level decision-making will be performed through system modeling as a Markov game in which game states are defined from human, environment, and aggregate MUS state. Our method abstracts MUS behaviors to reduce cognitive complexity and real-time network and computational overhead.

This project is developing theoretical foundations and computational algorithms for synthesizing higher-level supervisory and information-acquisition control logic in cyber-physical systems that expend or replenish their resources while interacting with the environment. On the one hand, qualitative requirements capture the safety requirements that are imposed on the system as it operates. On the other hand, quantitative requirements capture resource constraints in the context of energy-aware systems. These dual considerations are needed in applications of cyber-physical systems where efficient management of resources must be accounted for in the dynamic operation of the system in order to achieve the desired objectives within a given energy or resource budget. The approach pursued is formal and model-based. It leverages a recently-developed unified framework for supervisory control and information acquisition in the higher-level control logic of cyber-physical systems, but it explicitly embeds quantitative constraints in the solution procedure in order to capture the energy or resources expended and/or replenished by the cyber-physical system as it interacts with its environment. This generic solution methodology is applicable to several classes of cyber-physical systems subject to energy constraints. Software tools are being developed to facilitate the transition of these results to application domains. Of special interest is energy-aware mission planning in autonomous systems, a rich domain where qualitative mission requirements are coupled with quantitative constraints. Overall, this project impacts both the Science of Cyber-Physical Systems and the Engineering of Cyber-Physical Systems.

This CPS Frontiers project addresses highly dynamic Cyber-Physical Systems (CPSs), understood as systems where a computing delay of a few milliseconds or an incorrectly computed response to a disturbance can lead to catastrophic consequences. Such is the case of cars losing traction when cornering at high speed, unmanned air vehicles performing critical maneuvers such as landing, or disaster and rescue response bipedal robots rushing through the rubble to collect information or save human lives. The preceding examples currently share a common element: the design of their control software is made possible by extensive experience, laborious testing and fine tuning of parameters, and yet, the resulting closed-loop system has no formal guarantees of meeting specifications. The vision of the project is to provide a methodology that allows for complex and dynamic CPSs to meet real-world requirements in an efficient and robust way through the formal synthesis of control software. The research is developing a formal framework for correct-by-construction control software synthesis for highly dynamic CPSs with broad applications to automotive safety systems, prostheses, exoskeletons, aerospace systems, manufacturing, and legged robotics. The design methodology developed here will improve the competitiveness of segments of industry that require a tight integration between hardware and highly advanced control software such as: automotive (dynamic stability and control), aerospace (UAVs), medical (prosthetics, orthotics, and exoskeleton design) and robotics (legged locomotion). To enhance the impact of these efforts, the PIs are developing interdisciplinary teaching materials to be made freely available and disseminating their work to a broad audience. This is a continuing grant of Award # 1562236

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 project focuses on tackling the security and privacy of Cyber-Physical Systems (CPS) by integrating the theory and best practices from the information security community as well as practical approaches from the control theory community. The first part of the project focuses on security and protection of cyber-physical critical infrastructures such as the power grid, water distribution networks, and transportation networks against computer attacks in order to prevent disruptions that may cause loss of service, infrastructure damage or even loss of life. The second part of the project focuses on privacy of CPS and proposes new algorithms to deal with the unprecedented levels of data collection granularity of physical human activity. The work in these two parts focuses on the integration of practical control theory concepts into computer security solutions. In particular, in the last decade, the control theory community has proposed fundamental advances in CPS security; in parallel, the computer security community has also achieved significant advances in practical implementation aspects for CPS security and privacy. While both of these fields have made significant progress independently, there is still a large language and conceptual barrier between the two fields, and as a result, computer security experts have developed a parallel and independent research agenda from control theory researchers. In order to design future CPS security and privacy mechanisms, the two communities need to come closer together and leverage the insights that each has developed. This project attempts to facilitate the integration of these two communities by leveraging the physical properties of the system under control in two research problems: (1) Physics-based CPS security; and (2) Physics-based CPS privacy. Physics-based CPS security leverages the time series from sensor and control signals to detect deviations from expected operation. This is a growing area of research in both security and control theory venues, although there are several open problems in this space. This proposal tackles some of these open problems including the definition of new evaluation metrics that capture the unique operational properties of control systems, the consistent evaluation of different proposals for models and anomaly detection tests, and the development of new industrial control protocol parsers. Physics-based CPS privacy focuses on how to guide the implementation of general privacy recommendations like the Fair Information Practice principles into cyber-physical systems, leveraging the fact that these physical systems often have an objective to achieve, and this objective depends on the data-handling policies of the operator. The project focuses on investigating the trade-off between privacy and control performance and developing tools to guide how data minimization, data delays, and data retention should be implemented.

This project pursues a smart cyber-physical approach for improving the electric rail infrastructure in the United States and other nations. We will develop a distributed coordination of pricing and energy utilization even while ensuring end-to-end time schedule constraints for the overall rail infrastructure. We will ensure this distributed coordination through transactive control, a judicious design of dynamic pricing in a cyber-physical system that utilizes the computational and communication infrastructure and accommodates the physical constraints of the underlying train service. The project is synergistic in that it builds upon the expertise of the electric-train infrastructure and coordination at UIC and that of transactive control on the part of MIT. We will validate the approach through collaboration with engineers in the Southeastern Pennsylvania Transport Authority, where significant modernization efforts are underway to improve their electric-train system. The project also involves strong international collaboration which will also enable validation of the technologies. This project will formulate a multi-scale transitive control strategy for minimization of price and energy utilization in a geographically-dispersed railway grid with broader implications for evolving smart and micro grids. The transactions evolve over different temporal scales ranging from day-ahead offline transaction between the power grid and the railway system operators yielding price optimality to real-time optimal transaction among the trains or the area control centers (ACC). All of these transactions are carried out while meeting system constraints ranging from end-to-end time-scheduling, power-quality, and capacity. Our research focuses on fundamental issues encompassing integration of information, control, and power, including event-driven packet arrival from source to destination nodes while ensuring hard relative deadlines and optimal sampling and sensing; and formulation of network concave utility function for allocating finite communication-network capacity among control loops. The project develops optimization approaches that can be similarly applied across multiple application domains.

More than one million people including many wounded warfighters from recent military missions are living with lower-limb amputation in the United States. This project will design wearable body area sensor systems for real-time measurement of amputee's energy expenditure and will develop computer algorithms for automatic lower-limb prosthesis optimization. The developed technology will offer a practical tool for the optimal prosthetic tuning that may maximally reduce amputee's energy expenditure during walking. Further, this project will develop user-control technology to support user's volitional control of lower-limb prostheses. The developed volitional control technology will allow the prosthesis to be adaptive to altered environments and situations such that amputees can walk as using their own biological limbs. An optimized prosthesis with user-control capability will increase equal force distribution on the intact and prosthetic limbs and decrease the risk of damage to the intact limb from the musculoskeletal imbalance or pathologies. Maintenance of health in these areas is essential for the amputee's quality of life and well-being. Student participation is supported. This research will advance Cyber-Physical Systems (CPS) science and engineering through the integration of sensor and computational technologies for the optimization and control of physical systems. This project will design body area sensor network systems which integrate spatiotemporal information from electromyography (EMG), electroencephalography (EEG) and inertia measurement unit (IMU) sensors, providing quantitative, real-time measurements of the user's physical load and mental effort for personalized prosthesis optimization. This project will design machine learning technology-based, automatic prosthesis parameter optimization technology to support in-home prosthesis optimization by users themselves. This project will also develop an EEG-based, embedded computing-supported volitional control technology to support user?s volitional control of a prosthesis in real-time by their thoughts to cope with altered situations and environments. The technical advances from this project will provide wearable and wireless body area sensing solutions for broader applications in healthcare and human-CPS interaction applications. The explored computational methods will be broadly applicable for real-time, automatic target recognition from spatiotemporal, multivariate data in CPS-related communication and control applications. This synergic project will be implemented under multidisciplinary team collaboration among computer scientists and engineers, clinicians and prosthetic industry engineers. This project will also provide interdisciplinary, CPS relevant training for both undergraduate and graduate students by integrating computational methods with sensor network, embedded processors, human physical and mental activity recognition, and prosthetic control.