The safety and performance of cyber-physical systems (CPS) depend crucially on control and scheduling decisions that often are fixed at design time, which significantly restricts the conditions under which a system can operate both safely and with suitable performance. Going beyond prior work that has explored different control and scheduling adaptations in individual system designs, this project will conduct more general and in-depth investigations, into how cyber-physical systems? control and scheduling can be co-designed to adapt jointly, automatically, dynamically, safely, and effectively even in response to rapid, large, and diverse changes in: (1) the system?s controlled behavior; (2) its environment; (3) its physical components; and (4) its platform software and hardware. Our project will immerse multiple graduate students in cross-disciplinary research, with extensive education, training, and mentoring spanning computer science, control theory, natural hazards engineering, structural engineering, mechanical engineering, and computer engineering. We will also involve undergraduate students via summer REU supplements and in-semester mentored independent study projects for academic credit, and will leverage our existing initiatives and relationships with partner organizations for K-12 outreach. As we have done in each of our previous collaborations, our multi-university team will recruit, mentor, and retain participants from groups traditionally under-represented in science and technology fields, leveraging effective and established outreach programs at our institutions.

In this cross-disciplinary research project we will develop new formal models, analyses, system infrastructure, and evaluation metrics, to explicitly represent, respect, and even exploit control and scheduling inter-dependencies, to ensure that systems? behaviors remain safe while enabling significant improvements in performance. The novel co-design approach we propose will enable radically improved cyber-physical system performance capabilities while respecting safety constraints that may cross-cut cyber and physical components and the system?s environment. For example, it will enable more extreme (but safely realizable) stress testing and adaptive management of mechanical systems and civil structures, to gauge and maintain resilience to significant (potentially adverse) changes to conditions in a system and its environment, and to enact adaptive mitigating responses accordingly.

Aging civil infrastructure is a critical worldwide problem that affects daily life, making it important to innovate more efficient and economical repair and construction methods for civil structures. Additive manufacturing, or 3D printing, offers a promising way to fulfill this compelling need. However, almost all current additive manufacturing methods rely on gantry-based systems that can only build structures within rigid frames, thereby restricting printing speed and scale, thus hindering their use in maintenance and construction. This award supports fundamental research to establish collective additive manufacturing, a novel robotics-based approach for large-scale 3D printing. Collective additive manufacturing uses a team of autonomous mobile robots to jointly print large-scale 3D structures. The results of the research will have a potentially wide range of applications in civil infrastructure maintenance and construction, to post-disaster response and extraterrestrial construction. The project is based on a convergent research approach involving robotics, artificial intelligence, control theory, and dynamical systems, which culminates in formal and informal learning activities to broaden participation of underrepresented groups in engineering.

Collective additive manufacturing envisions the use of teams of mobile robots to overcome key limitations of existing gantry-based additive manufacturing, including its small scale and slow printing speed. To unleash the full potential of collective additive manufacturing, several scientific boundaries must be pushed, ensuring optimal deployment of multiple mobile robots that print large structures according to an engineered, virtual design. This research will fill critical knowledge gaps in robotic localization, control, and coordination, to realize a robotic team that intentionally and actively modifies its surroundings to successfully complete its printing task. This interdisciplinary research program will unfold along three thrusts: artificial intelligence for planning and localization, model predictive control to adapt to printing disturbances and substrate variations, and distributed control to elicit stable collective dynamics. Theoretical advancements will proceed alongside with experimental research toward demonstrating the potential of collective additive manufacturing to accurately and efficiently print large structures in real-world settings.

Loss of walking function and leg sensation are devastating consequences of spinal cord injury (SCI). These deficits have a profoundly negative impact on independence and quality of life of those affected. Moreover, wheelchair reliance after SCI increases the risk of medical complications. The healthcare costs associated with SCI are ~$50 billion/year, presenting a significant public health concern. Currently, there are no biomedical solutions capable of restoring walking and leg sensation after SCI. Clinically practical and socially acceptable solutions to these important problems are desperately needed. Employing a cyber-physical system (CPS) to bypass the damaged spinal cord may be a novel way to restore walking and leg sensation to those with leg paralysis due to SCI. The proposed multi-disciplinary effort will inspire students from traditionally underprivileged and underrepresented groups to pursue college education in STEM fields by demonstrating how engineering and science can make a difference in the well-being of those with disabilities. In addition, it will engage individuals with disabilities, their family members, friends, and caregivers, in educational opportunities in order to increase their scientific and technical literacy. The outreach to these communities will be accomplished by leveraging diverse ethnic makeup of Orange and Los Angeles Counties, geographic proximity of the three study sites, which makes outreach activities amenable to integration, and the high societal significance and visibility of the project. 


Impairment or complete loss of gait function and lower extremity sensation are common after spinal cord injury (SCI). A new cyberphysical system, CPS, can be realized as a permanently implantable bi-directional (BD) brain-computer interface (BCI), which translates walking intentions from brain signals into commands for a leg prosthesis, and converts prosthesis sensor signals into electrical stimulation of the brain for artificial leg sensation. This closed-loop operation would come close to restoring able-body-like walking and leg sensation after SCI. Before such an implantable CPS is deployed in humans, its feasibility and safety must be established. The main objective of this Frontier project is to design, develop, and test a wearable analogue of a fully implantable electrocorticogram (ECoG)-based BD-BCI for walking and leg sensation. The BD-BCI CPS will be designed as an ultra-low power modular system with revolutionary techniques for interference mitigation to enable simultaneous electrical stimulation and recording. The first module will consist of a custom brain signal acquisition system that exploits ECoG signal attributes to significantly reduce power consumption. The second module will consist of a low-power processing unit, brain stimulator, and wireless communication transceiver. This module will internally execute optimized BCI algorithms and wirelessly transmit commands to a robotic gait exoskeleton for walking. Comprehensive benchtop and bedside tests will be conducted to assess proper system function. Finally, subjects with paraplegia due to SCI will be recruited to undergo a 30-day ECoG implantation to test the BD-BCI's ability to restore brain-controlled walking and leg sensation. The goals of transition to practice (TTP) are to: (1) develop a fully implantable version of the BD-BCI, (2) perform a series of industrial-standard medical device benchtop tests, and (3) test the implants safety.

The adoption of cyber-physical systems (CPS) is growing at an accelerated rate, giving rise to large-scale CPS??for example, comprised of large numbers of robots in a warehouse, turbines on a wind farm, or vehicles and traffic signals in a city. If intelligently coordinated, these systems will unlock transformative societal benefits across broad economic sectors. They also promise to contribute to the most pressing challenge of the century??climate change??by substantially utilizing resources more effectively, such as from supply chains, energy systems, and urban systems. Unfortunately, effective coordination schemes have been elusive due to the sheer scale and diversity of scenarios that these systems encounter. To advance robust coordination in large-scale CPS, this project investigates the generalization of learning-enabled methods as a key solution concept, in light of their potential to translate coordination schemes across disparate scenarios. The project's impact will be enhanced through the dissemination of open-source research and teaching material, and via experiments derived from large-scale CPS applications in collaboration with public sector, industry, and academic partners. The project also boosts K-12, undergraduate, graduate, and professional education, by supporting and actively engaging students in research activities, promoting the translation of research to practice, and through outreach efforts targeting middle school students from underrepresented and underserved communities.

This NSF CAREER project focuses on an enabling methodology for large-scale CPS: understanding generalization of learning-enabled methods, and further applying it to reduce the complexity of system design and analysis. Recent evidence shows that controllers trained using machine learning sometimes have the remarkable ability to generalize to other scenarios, such as to different problem sizes or between simulated and physical robotic systems. However, generalization is currently more of an art than a science; the conditions under which generalization is successful are not well understood. At the same time, large-scale CPS often induce parameterized families of scenarios; for example, traffic control must consider different weather conditions, sensing modalities, and numbers and types of agents. This family of related CPS scenarios thus provides a platform for carefully examining generalization across scenarios. The project will: 1) advance learning algorithms for large-scale CPS by designing coordination-aware model-based reinforcement learning methods for multi-agent systems; 2) leverage the algorithms to understand generalization by formalizing, measuring, and characterizing generalization with respect to deviations in CPS scenarios; and then 3) harness generalization for robust coordination, by efficiently solving large families of scenarios necessary to provide high performance and assurances.

Emerging mobility systems, e.g., connected and automated vehicles and shared mobility, provide the most intriguing opportunity for enabling users to better monitor transportation network conditions and make better decisions for improving safety and transportation efficiency. However, different levels of vehicle automation in the transportation network can significantly alter transportation efficiency metrics (travel times, energy, environmental impact). Moreover, we anticipate that efficient transportation might alter human travel behavior causing rebound effects, e.g., by improving efficiency, travel cost is decreased, hence willingness-to-travel is increased. The latter would increase overall vehicle miles traveled, which in turn might negate the benefits in terms of energy and travel time. The project will consolidate emerging mobility systems and modes with real-world data and processed information leading to an equitable transportation system with broad economic, environmental, and societal benefits. We expect the outcome of this project to enhance our understanding of the rebound effects, changes in travel demand and capacity, human reception, adoption, and use of emerging mobility systems. 

The outcome of this research will deliver an online learning framework that will aim at distributing travel demand in a given transportation network resulting in a socially-optimal mobility system that travelers would be willing to accept. A ?socially-optimal mobility system? is defined as a mobility system that (1) is efficient (in terms of energy consumption and travel time), (2) does not cause rebound effects, and (3) ensures equity in transportation. The framework will establish new approaches in optimally controlling cyber-physical systems by merging learning and control approaches. It includes the development of new methods to enhance accessibility, safety, and equity in transportation and travelers? acceptance. In the context of the proposed framework, a ?social planner? faces the problem of aggregating the preferences of the travelers into a collective, system-wide decision when the private information of the travelers is not publicly known. Mechanism design theory will be used to derive the optimal routes and the selection of a transportation mode for all travelers so as to maximize accessibility, safety, and equity in transportation and travelers? acceptance. Online learning algorithms for contextual bandit problems will be developed to identify traveler preferences and to determine how they would respond to the social planner?s recommendations on routing and selection of a transportation mode.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

The goal of this work is the realization of autonomous cyber-physical, microsystems for automated correction of blurred vision using flexible electronic contact lenses. The vision correction system integrates thin variable power lenses with object distance microsensors and computation and control software to continuously produce sharply-focused images in individuals suffering from presbyopia. Presbyopia, or loss of the eye's ability to change focus, is an inevitable and universal age-related condition that affects aging adults, causing blurred images and visual impairment. In 2018, two billion people worldwide were estimated to suffer from presbyopia. The proposed vision correction microsystem operates autonomously while collecting energy from its surrounding environment in order to provide continuous vision correction for an entire day. Realization of such microsystems advances our scientific knowledge of autonomous microsystem engineering for medical applications, ultimately improving the daily lives and well-being of billions of aging adults while reducing the cost of treatment.

The implementation of autonomous microsystems for vision correction requires deep integration of ultrathin state-of-the-art inhomogeneous microtechnologies including variable power liquid-crystal lenses, paper-thin embedded microprocessors and communications circuits with tens of millions of transistors, paper-thin microsensors to detect light level, user orientation and focal distance, and thin photovoltaic cells with power management circuits, all integrated onto a single flexible package that conforms to the surface of the human eye. The system must be able to scavenge power and manage its operation completely autonomously, in the best possible way, in a resource-limited biological environment. Advancements in systems with these characteristics are widely applicable to many future cyber physical systems (CPS) for medical and health monitoring applications. This extremely ambitious project pushes the frontiers of inhomogeneous microtechnology integration to a level that has been repeatedly dreamed of, but never realized before, to produce a highly integrated CPS that can benefit billions of people.

Complex cyber-physical systems (CPS) that operate in dynamic and uncertain environments will inevitably encounter unanticipated situations during their operation. Examples range from naturally occurring faults in both the cyber and physical components to attacks launched by malicious entities with the purpose of disrupting normal operations. As infrastructures, e.g. energy, transportation, industrial systems and built environments, are getting smarter, the chance of a fault or attack increases. When this happens, it is essential that system behavior remains viable, i.e., it does not violate pre-specified operating constraints on run-time behavior. Preserving safety, for instance, is of paramount importance to avoid damage and possible loss of life. This project will develop strategies for mitigating the effects of such unanticipated situations, that seek to optimize for performance (measured by multiple metrics such as cost, efficiency, accuracy, etc.) without compromising viability. The emphasis will be on the automotive application domain, given the upcoming revolution brought by innovations such as vehicle-to-vehicle (V2V), vehicle to infrastructure (V2I) communication and autonomous driving, and because of the safety-criticality of the transportation infrastructure. To ground our research on relevant problems, we will engage industrial partners. The outcomes of the project will be validated upon test scenarios drawn from the automotive industry. 

Fundamental issues arising when safety-critical CPS operate in uncertain environments will be addressed, with the objective of obtaining a better understanding of, and developing optimal or near-optimal strategies for dealing with, emergent problems that arise from the interaction of resource-allocation and control strategies in such systems. One of the novelties of the technical approach adopted in this project is to closely integrate three different CPS perspectives ? control theory, automotive & aerospace application domain-knowledge, and real-time resource management & scheduling ? in order to develop run-time mitigation strategies for complex CPS's operating in dynamic and uncertain environments, and exposed to a variety of faults. Such an integrated approach will allow for the identification of emergent problems that arise from the interaction of resource-allocation and control algorithms, that may otherwise remain undiscovered if the control and resource-allocation aspects were considered separately.
The general design-time and run-time tools for creating resilient CPSs will be guided by the implementation and evaluation of the research in simulation and on laboratory test-beds upon three applications from the automotive domain: fault resilience for variable-valve internal combustion engines; fail-safe energy management for hybrid-electric vehicles; and robust sensor management for autonomous vehicles.

This Cyber-Physical Systems (CPS) grant will advance structural health monitoring of concrete structures by relying on data acquired by a novel sensing technology with unprecedented scalability and spatial resolution. Modern society depends critically on sound and steadfast functioning of a variety of engineering structures and infrastructures, such as bridges, buildings, pipelines, geotechnical structures, aircrafts, wind turbines, and industrial facilities. Due to aging, massive urbanization, and climate change, there is a growing need for accurate and reliable assessment of the health condition, performance, and operation of these structures in order to ensure their continuous functioning and safe use. The researched technology enables pervasive and scalable sensing of concrete structures with high resolution by transforming concrete into a smart self-sensing material, thereby enabling reliable long-term structural health monitoring. This in turn contributes to the nation?s sustainability and resilience and to advancing the nation?s prosperity, welfare, and security. The project advances multiple core research areas in structural health monitoring including CPS system architectures using embedded devices, multi-parameter sensing and networking based on radio frequency sensors, and machine learning for accurate and reliable data analytics. The research outcomes are highly translational to various other CPS domains. The project also contributes to secondary education and outreach activities in multiple ways as well as to undergraduate and graduate education. 

The aim of this project is to create a novel sensing system comprised of radio frequency sensors that are pervasively embedded in large volumes of concrete structures and that sense their localities using radio frequency properties. The objective is the assessment of key parameters that reflect the behavior of the monitored structure under operational conditions, such as deformation, temperature, and humidity, as well as detection and characterization of damages. The project has the following intellectual contributions: 1) Passive radio frequency-based sensing that operates over a wide range of frequencies; architectures of smart exciters and networked radio frequency sensors that communicate among themselves via backscatter modulation; solar-powered radio frequency exciter platform that powers the sensors. 2) Energy-based sensing and network optimization of the radio frequency sensor network in terms of its monitoring ability and network connectivity given the constraints on the available harvested power at the exciters. 3) Machine learning methods for function estimation based on the principle of ensemble modeling with Gaussian processes and applied to self-localization and to inference of three-dimensional distributions of material parameters within large volumes of concrete structures.

The reach of cyber-physical systems into space is growing exponentially, as launch services proliferate and satellites have become small, cheap, and capable. Unlike expensive satellites of the past, the near future promises constellations of thousands of inexpensive nanosatellites. Nanosatellites are becoming capable of supporting space-based cyber-physical applications, including defense, smart cities, agriculture, & infrastructure, climate science, and search & rescue. Unfortunately, nanosatellites today operate like monolithic satellites of the past, manually operated at high cost, impeding the realization of the potential of nanosatellites for these important cyber-physical systems applications. This project envisions a new operating model for space-based cyber-physical systems in nanosatellite constellations called a "software-defined nanosatellite constellation", which unleashes their potential. A software-defined constellation is a collection of nanosatellites capable of autonomously sensing the environment, processing data, and cooperatively planning and taking mechanical actions. The project realizes this vision through cross-cutting cyber-physical systems research spanning computer systems, control, planning, actuation, machine learning, and communications. This project will culminate in the launch of a software-defined constellation testbed that implements several space-based applications, demonstrating these societally important capabilities and applications, and functioning as a valuable resource for other cyber-physical systems researchers.

This CPS Frontier project is establishing nanosatellite constellations as sophisticated, multi-tenant platforms for space-based cyber-physical systems applications. The work is interdisciplinary, spanning controls, ML, communications, systems, and hardware. The project makes constellations autonomous and equipped to compute efficiently on orbit. On-orbit computing treats constellation-level satellite control and actuation as resource management for unique nanosatellite resources: sensor data, bandwidth, energy, and computing. On-orbit machine learning techniques bring federated learning to the constellation, creating an autonomous orbital learning system. The project?s new communication techniques extract maximum information from each bit communicated, combining weak signals and often avoiding communication altogether. The project demonstrates the project?s value with on-orbit infrastructure and testbeds that form an open platform for future space-based cyber-physical systems research. The project will have a broad, transformative impact on society, industry, and education, within and beyond the cyber-physical and space systems communities. Software-defined nanosatellite constellations create an industry of cost-effective space-based applications. The project eliminates barriers to space, enabling industry to develop space-based applications. The project creates a new field of research around space-based cyber-physical systems fostering research and education. This project includes ambitious education activities from middle school to post-graduate levels. The project team will mentor middle schoolers and high schoolers from urban public schools through space systems research internships. The project team will involve undergraduates and graduate students in research mentoring and new curricula. The proposed outreach activities will engage society broadly via artist-in-residence programs, research community-building, and public/academic/private partnerships.

This project develops a cyber-physical-social system for communities to incentivize emerging ride-sourcing (such as Uber and Lyft) and sharing services to improve societal outcomes. The goal is to enable novel public-private partnerships that leverage these services for a win-win-win outcome for all parties involved: reducing travel times, energy use and emissions, while ensuring cost-effectiveness for public agencies; boosting mobility service providers' profitability; and improving the experience of all travelers. Research results will be disseminated through courses, open-source tools, journal publications, conferences, workshops, and an online short course. This project will provide interdisciplinary training to a diverse group of students, who will be part of the next generation of globally-engaged leaders. Learning sessions and hands-on activities will be designed and offered to general public under the collaboration with the Carnegie Museum of Natural History. 

This project develops a theoretical, modeling, and computational framework for communities to incentivize emerging mobility services to achieve system-wide goals on efficiency and reliability. This is done through optimally pricing a surcharge or credit to riders' fare with respect to departure times, routes, pooling and curbs (i.e., pick-up/drop-off locations), in conjunction with subsidies to mobility service providers in exchange for guaranteed system improvement. This project advances fundamental knowledge regarding how public right-of-way spaces (such as curbs and roads) and travel demand should be priced and balanced for social optimum. It develops an architecture that integrates travelers' seeking to maximize their utilities and service providers' goals for improving service efficiency and maximizing revenue, with novel optimization and controls of infrastructure and service pricing. In addition, it develops efficient and scalable algorithms to estimate and optimize mixed flows of shared and personal vehicles for large-scale networks. This project will assess multi-source high-resolution data, including vehicle trajectory data from mobility service providers to validate, test and demonstrate this cyber-physical-social system.