Harnessing wind energy is one of the pressing challenges of our time. The scale, complexity, and robustness of wind power systems present compelling cyber-physical system design issues. Leveraging the physical infrastructure at Purdue, this project aims to develop comprehensive computational infrastructure for distributed real-time control. In contrast to traditional efforts that focus on programming-in-the-small, this project emphasizes programmability, robustness, longevity, and assurance of integrated wind farms. The design of the proposed computational infrastructure is motivated by, and validated on, complex cyber-physical interactions underlying Wind Power Engineering. There are currently no high-level tools for expressing coordinated behavior of wind farms. Using the proposed cyber-physical system, the project aims to validate the thesis that integrated control techniques can significantly improve performance, reduce downtime, improve predictability of maintenance, and enhance safety in operational environments. The project has significant broader impact. Wind energy in the US is the fastest growing source of clean, renewable domestically produced energy. Improvements in productivity and longevity of this clean energy source, even by a few percentage points will have significant impact on the overall energy landscape and decision-making. Mitigating failures and enhancing safety will go a long way towards shaping popular perceptions of wind farms -- accelerating broader acceptance within local communities. Given the relative infancy of "smart" wind farms, the potential of the project cannot be overstated.

This CAREER project responds to an urgent need to develop mobile power distribution systems that lower deployment and operating costs while simultaneously increasing network efficiency and response in dynamic and often dangerous physical conditions. The significant need for an efficient and effective mobile power distribution system became evident during search and rescue/recovery missions following the Japan tsunami and the disappearance of the Malaysia MH370 airplane. The technology outcomes from this project will apply to a broad range of environments (in space, air, water or on ground) where the success of long-term robotic network missions is measured by the ability of the robots to operate, for an extended period of time, in highly dynamic and potentially hazardous environments. These advanced features will provide the following advantages: efficiency, efficacy, guaranteed persistence, enhanced performance, and increased success in search/rescue/recovery/discovery missions. Specifically, this project addresses the following technology problems as it translates from research discovery toward commercial application: inflated energy use currently required when the autonomous vehicles break from mission to return to recharging station; lack of multi-robot coordination needed to take into account both fundamental hardware and network science challenges necessary to respond to energy needs and dynamic environment conditions. By addressing these gaps in technology, this work establishes the theoretical, computational, and experimental foundation for mobile power delivery and onsite recharging capability. Moreover, the new technology developed in this project is universally adaptable for disparate autonomous vehicles especially autonomous underwater vehicles (AUVs). In more technical terms, this project creates network optimization and formation strategies that will enable a power distribution system to reconfigure itself depending on the number of operational autonomous vehicles and recharging specifications to meet overall mission specifications, the energy consumption needs of the network, situational conditions, and environmental variables. Such a system will play a vital role in real-time controlled applications across multiple disciplines such as sensor networks, robotics, and transportation systems where limited power resources and unknown environmental dynamics pose major constraints. In addition to addressing technology gaps, undergraduate and graduate students will be involved in this research and will receive interdisciplinary education/ innovation/ technology translation/ outreach experiences through: developing efficient network energy routing, path planning and coordination strategies; designing and creating experimental test-beds and educational platforms; and engaging K-12th grade students in Science, Technology, Engineering and Math including those from underrepresented groups. This project engages Michigan Tech's Great Lake Research Center (GLRC) and Center for Agile Interconnected Microgrids (AIM) to develop experimental test-beds and conduct tests that validate the resulting methods and algorithms, and ultimately, facilitate the technology translation effort from research discovery toward commercial reality.

Title: CPS: Breakthrough: Compositional Modeling of Cyberphysical Systems This project is devoted to the discovery of new mathematical modeling techniques for Cyber-Physical Systems. In particular, the research involves devising novel conceptual methods for assembling systems from subsystems, and for reasoning about the behavior of systems in terms of the behavior of their subsystems, which may be computational or physical. The results enable scientists and engineers to develop more realistic models of the systems they are designing, and to obtain greater insights into their eventual behavior, without having to build costly prototypes. The intellectual merits are the new notions of system behavior being developed that unify the computational and the physical, and the mathematical operators and laws governing the relationships between systems and subsystems. The project's broader significance and importance lie in the increased pace of innovation within Cyber-Physical System design that the new modeling techniques make possible, and the curricular enhancements that the novel conceptual frameworks under development support. The specific research program of this project involves the development of a novel modeling paradigm, Generalized Synchronization Trees (GSTs), into a rich framework for both describing Cyber-Physical Systems (CPSs) and studying their behavior under interconnection. GSTs are inspired by Milner's use of Synchronization Trees (STs) to model interconnected computing processes, but GSTs generalize the mathematical structure of their forebears in such a way as to encompass systems with discrete ("Cyber") as well as continuous ("Physical") dynamics. As Milner did with STs, the PIs are developing an algebraic theory of composition for GSTs. Such theories have a particular advantage over non-algebraic ones: because the composition of two (or more) objects results in an object of the same type, composition operators can be nested to build large structures out of smaller ones. Thus, the theory of GSTs is inherently compositional. The development of the theory involves five distinct but complementary endeavors. Standard models for cyber-physical systems are being encoded as GSTs in a semantically robust way; meaningful notions of composition and congruence for CPSs are being described and studied algebraically; the interplay between behavioral equivalence and the preservation of system properties is being investigated; a notion of real-time (or clock time) is under consideration for GSTs; and GSTs are being assessed as modeling tools for practical design scenarios.

Traditionally, the design of urban transit services has been based on limited sampling data collected through surveys and censuses, which are often dated and incomplete. Lacking massive online feeds from multiple transit modes makes it hard to achieve real-time equilibrium in demand and supply relationship through cyber-control, which eventually manifests into multiple urban transportation issues: (i) lengthy last-mile transit due to non-supply, (ii) prolonged waiting due to undersupply, and (iii) excessive idle mileage due to oversupply. This project addresses these issues by focusing on two types of transportation systems in metropolitan areas: (i) public bike rental sharing systems and (ii) fleet-oriented ride sharing systems. The public bike rental sharing systems are used to allow commuters to rent bikes near public transit stations for the last mile of their trips. The fleet-oriented ride sharing systems schedule a fleet of participating vehicles for ride sharing among passengers in which shared ridership reduces individual fare paid by passengers, increases the profit of taxi drivers, and can improve the availability of service. The theory and practice of transportation sharing systems have typically focused on isolated individual transportation modes. The project will collect massive multi-modal online feeds from metropolitan information infrastructure to model dynamic behaviors of transportation systems, and then utilize massive micro-level trip information to apply fine-grained real-time control to handle rapid changes in dynamic metropolitan environments. General principles and design methodologies will be designed to build multi-modal, integrated urban transportation systems. These research discoveries will be applied toward commercial applications. Long-term deployment problem of bike stations will be addressed, especially in the low-income districts, to provide suggestions on the station deployment and assessment for specific deployment plans. The project also solves the short-term bike maintenance issue to balance the usage of shared bikes to prevent quick deterioration of rental bikes, and improve availability of bike rental services in real time. This project will also study fleet-oriented ride sharing systems that decide fares based on real-time supply/demand ratio to handle dynamic metropolitan scenarios. This project will support two Ph.D. students who will receive innovation and technology translation training through close interactions with municipal governments and small-business companies. Such partnerships expedite the adoption of cutting-edge technology, evaluate research solutions in operational environments, and obtain user feedback to trigger further innovations. The project will improve the efficiency of existing transportation systems under sharing economy and ultimately the work would noticeably improve the quality of every-day life in metropolitan areas across the United States.

1446582 (Shroff) and 1446478 (Hou). Buildings in the U.S. contribute to 39% of energy use, consume approximately 70% of the electricity, and account for 39% of CO2 emissions. Hence, developing green building architectures is an extremely critical component in energy sustainability. The investigators will develop a unified analytical approach for green building design that comprehensively manages energy sustainability by taking into account the complex interactions between these systems of systems, providing a high degree of security, agility and robust to extreme events. The project will serve to advance the general science in CPS, help bridge the gap between the cyber and civil infrastructure communities, educate students across different disciplines, include topics in curriculum development, and actively recruit underrepresented minority and undergraduate students. The main thesis of this research is that ad hoc green energy designs are often myopic, not taking into account key interdependencies between subsystems and users, and thus often lead to undesirable solutions. In fact, studies have shown that 28%-35% of LEED-certified buildings consumed more energy than their conventional counterparts, all of which calls for the development of a comprehensive analytical foundation for designing green buildings. In particular, the investigators will focus on three interrelated thrust areas: (i) Integrated energy management for a single-building, where the goal is to jointly consider the complex interactions among building subsystems. The investigators will develop novel control schemes that opportunistically exploit the energy demand elasticity of the building subsystems and adapt to occupancy patterns, human comfort zones, and ambient environments. (ii) Managing multi-building interactions to develop (near) optimal distributed control and coordination schemes that provide performance guarantees. (iii) Designing for anomalous conditions such as extreme weather and malicious attacks, where power grid connections and/or cyber-networks are disrupted. The research will provide directions at developing an analytical foundation and cross-cutting principles that will shed insight on the design and control of not only building systems, but also general CPS systems. An important goal is to help bridge the gap between the networking, controls, and civil infrastructure communities by giving talks and publishing works in all of these forums. The investigators will disseminate the research findings to industry as well as offer education and outreach programs to the K-12 students in STEM disciplines. The investigators will also actively continue their already strong existing efforts in recruiting women and underrepresented minorities, as well as providing rich research experience to undergraduate REU students. This project will provide fertile training for students spanning civil infrastructure research, networking, controls, optimization, and algorithmic development. The investigators will also actively include the outcomes of the research in existing and new courses at both the Ohio State University and Virginia Tech.

Stroke is the leading cause of long-term disability in the US with approximately 7 million stroke survivors living in the US today and for patients with neurological disorders, it has been shown that limited gait velocity commonly results in walking that is predominantly restricted to the household. Unlike traditional exoskeletons which contain rigid linkage elements, the vision for this work is for exosuits that use soft materials such as textiles to provide a more conformal, unobtrusive and compliant means to interface to the human body. This represents a fundamental change in the paradigm of how people have viewed and designed wearable robots for the last half a century. Such a solution would have broad impact beyond the stroke patient population and could provide benefit to children with Cerebral Palsy or elderly individuals with muscle weakness. In addition there are plans to create a set of novel instructional educational toolkits for patient-in-the-loop cyber-physical systems that will be shared via an online portal and the CPS Virtual Organization (CPS-VO). With a patient-in-the-loop CPS, the patient, the physical suit, the computational control algorithms and the task/environment form a system in which all of the elements need to seamlessly interact. Through a modeling and experimental approach involving extensive human subjects studies, the team aims to create a unified engineering, biomechanical and physiological framework for designing and evaluating patient-in-the-loop CPS that include co-operative controllers that adapt in real-time to the patient to ensure safety and reliability an integrated system. Specifically the project will seek to gain a fundamental understanding of how to (1) analytically and experimentally characterize how forces are transmitted from these soft systems to the patient through the underlying soft tissue so as to generate assistance, (2) apply the optimal magnitude and timing of assistance to the patient to promote a more symmetric and natural gait by monitoring biomechanical, physiological and suit sensor data and (3) fuse information from different sensors monitoring patient motion and interaction forces to create an integrated CPS with a co-operative controller than can adapt to non-periodic movements of the patient.

This project investigates new reinforcement learning algorithms to enable long-term real-time autonomous learning by cyber-physical systems (CPS). The complexity of CPS makes hand-programming safe and efficient controllers for them difficult. For CPS to meet their potential, they need methods that enable them to learn and adapt to novel situations that they were not programmed for. Reinforcement learning (RL) is a paradigm for learning sequential decision making processes with potential for solving this problem. However, existing RL algorithms do not meet all of the requirements of learning in CPS. Efficacy of the new algorithms for CPS is evaluated in the context of smart buildings and autonomous vehicles. Cyber-physical systems (CPS) have the potential to revolutionize society by enabling smart buildings, transportation, medical technology, and electric grids. Success of this project could lead to a new generation of CPS that are capable of adapting to their situation and improving their performance autonomously over time. In addition to the traditional methods of dissemination, this project will develop and release open-source code implementing the new reinforcement learning algorithms. Education and outreach activities associated with the project include a Freshman Research Initiative course, participation in a UT Austin annual open house that draws in many underrepresented minorities to interest the public in computer science and science in general, and the department's annual summer school for high school girls called First Bytes.

Objective: How much a person should be allowed to interact with a controlled machine? If that machine is safety critical, and if the computer that oversees its operation is essential to its operation and safety, the answer may be that the person should not be allowed to interfere with its operation at all or very little. Moreover, whether the person is a novice or an expert matters. Intellectual Merit: This research algorithmically resolves the tension between the need for safety and the need for performance, something a person may be much more adept at improving than a machine. Using a combination of techniques from numerical methods, systems theory, machine learning, human-machine interfaces, optimal control, and formal verification, this research will develop a computable notion of trust that allows the embedded system to assess the safety of the instruction a person is providing. The interface for interacting with a machine matters as well; designing motions for safety-critical systems using a keyboard may be unintuitive and lead to unsafe commands because of its limitations, while other interfaces may be more intuitive but threaten the stability of a system because the person does not understand the needs of the system. Hence, the person needs to develop trust with the machine over a period of time, and the last part of the research will include evaluating a person's performance by verifying the safety of the instructions the person provides. As the person becomes better at safe operation, she will be given more authority to control the machine while never putting the system in danger. Broader Impacts: The activities will include outreach, development of public-domain software, experimental coursework including two massive online courses, and technology transfer to rehabilitation. Outreach will include exhibits at the Museum of Science and Industry and working with an inner-city high school. The algorithms to be developed will have immediate impact on projects with the Rehabilitation Institute of Chicago, including assistive devices, stroke assessment, and neuromuscular hand control. Providing a foundation for a science of trust has the potential to transform rehabilitation research.

This project will develop architecture and supporting enabling technologies to avert imminent loss of life or property in fast changing environments. The selected application is resuscitation in an intensive care unit (ICU) because it is life critical, time critical, human-centric and includes complex devices and software. For example, heart attack can be obscured in a trauma patient hemorrhaging from a broken leg in the presence of a collapsed lung. The challenge lies in solving the overarching difficulties of safe execution while maintaining complex and dynamic workflows. The availability and skill levels of medical staff, patient conditions, and medical device configurations all change rapidly. The core contribution is design and verification of reduced complexity situation awareness architecture for Emergency Cyber Physical Human systems (ECPH), supported by enabling technologies such as workflow adaptation protocols, managing data uncertainty and safe device plug and play. The ECPH workflow adaptation protocols are not only a function of the tasks and environment at hand, but must also be aware of the capabilities and training of the medical staff. In addition, risk mitigation driven safety interlock protocols will keep the actions of medical staff and CPS in synchrony with dynamically selected workflows. This is a cooperative effort of UIUC engineering and the ICU department of Carle Foundation Hospital. An ECPH team operates to accomplish a mission under rapidly changing circumstances. The stressful, rushed, and often unfriendly environment of an ECPH system means that errors, uncertainty, and failures will arise. This research will offer safety and resilience in the face of such disruptions. Effective and immediate intervention enabled by an optimized ECPH system will dramatically reduce preventable errors. The societal impact of effective collaboration under high stress will be enormous in terms of human lives and health care costs. According to CDC in 2010, the estimated direct & indirect costs of heart attacks and strokes alone in the U.S. were $503.2 billion; a significant percent of such patients during emergency care suffer complications and harm which are preventable. This project will develop educational material for training the next generation of researchers and engineers. The technology to be developed will also be adapted to other similar ECPH environments such as fighting a raging building fire.

This project will result in fundamental physical and algorithmic building blocks of a novel cyber-physical for a two-way communication platform between handlers and working dogs designed to enable accurate training and control in open environments (eg, disaster response, emergency medical intervention). Miniaturized sensor packages will be developed to enable non- or minimally-invasive monitoring of dogs' positions and physiology. Activity recognition algorithms will be developed to blend data from multiple sensors. The algorithms will dynamically determine position and behavior from time series of inertial and physiological measurements. Using contextual information about task performance, the algorithms will provide duty-cycling information to reduce sensor power consumption while increasing sensing specificity. The resulting technologies will be a platform for implementation of communication. Strong interactions among computer science, electrical engineering, and veterinary science support this project. Work at the interface between electrical engineering and computer science will enable increased power efficiency and specificity of sensing in the detectors; work at the interface of electrical engineering and veterinary behavior will enable novel physiological sensing packages to be developed which measure behavioral signals in real time; Project outcomes will enable significant advances in how humans interact with both cyber and physical agents, including getting clearer pictures of behavior through real time physiological monitoring. Students are part of the project and multidisciplinary training will help to provide development of the Cyber-Physical Systems pipeline. Project outreach efforts will include working with middle school children, especially women and under-represented minorities, presentations in public museums that will promote public engagement and appreciation of the contribution of cyber-physical systems to daily lives. The goal of each outreach activity is to encourage both interest and excitement for STEM topics, demonstrating how computer science and engineering can lead to effective and engaging cyber-physical systems.