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.

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.

As self-driving cars are introduced into road networks, the overall safety and efficiency of the resulting traffic system must be established and guaranteed. Numerous critical software-related recalls of existing automotive systems indicate that current design practices are not yet up to this challenge. This project seeks to address this problem, by developing methods to analyze and coordinate networks of fully and partially self-driving vehicles that interact with conventional human-driven vehicles on roads. The outcomes of the research are expected to also contribute to the safety of other cyber-physical systems with scalable configurable hierarchical structures, by developing a mathematical framework and corresponding software tools that analyze the safety and reliability of a class of systems that combine physical, mechanical and biological components with purely computational ones. The project research spans four technical areas: autonomous and human-controlled collaborative driving; scheduling computations over heterogeneous distributed computing systems; security and trust in V2X (Vehicle-to-Vehicle and Vehicle-to-Infrastructure) networks; and Verification & Validation of V2X systems through semi-virtual environments and scenarios. The integrating aspect of this research is the development of a distributed system calculus for Cyber-Physical Systems (CPS) that enables modeling, simulation and analysis of collaborative vehicular systems. The development of a comprehensive framework to model, analyze and test reconfiguration, hierarchical control, security and trust differentiates this research from previous attempts to address the same problem. Educational and outreach activities include integration of project research in undergraduate and graduate courses, and a summer camp at Ohio State University for high-school students through the Women in Engineering program.

This project represents a cross-disciplinary collaborative research effort on developing rigorous, closed-loop approaches for designing, simulating, and verifying medical devices. The work will open fundamental new approaches for radically accelerating the pace of medical device innovation, especially in the sphere of cardiac-device design. Specific attention will be devoted to developing advanced formal methods-based approaches for analyzing controller designs for safety and effectiveness; and devising methods for expediting regulatory and other third-party reviews of device designs. The project team includes members with research backgrounds in computer science, electrical engineering, biophysics, and cardiology; the PIs will use a coordinated approach that balances theoretical, experimental and practical concerns to yield results that are intended to transform the practice of device design while also facilitating the translation of new cardiac therapies into practice. The proposed effort will lead to significant advances in the state of the art for system verification and cardiac therapies based on the use of formal methods and closed-loop control and verification. The animating vision for the work is to enable the development of a true in silico design methodology for medical devices that can be used to speed the development of new devices and to provide greater assurance that their behaviors match designers' intentions, and to pass regulatory muster more quickly so that they can be used on patients needing their care. The scientific work being proposed will serve this vision by providing mathematically robust techniques for analyzing and verifying the behavior of medical devices, for modeling and simulating heart dynamics, and for conducting closed-loop verification of proposed therapeutic approaches. The acceleration in medical device innovation achievable as a result of the proposed research will also have long-term and sustained societal benefits, as better diagnostic and therapeutic technologies enter into the practice of medicine more quickly. It will also yield a collection of tools and techniques that will be applicable in the design of other types of devices. Finally, it will contribute to the development of human resources and the further inclusion of under-represented groups via its extensive education and outreach programs, including intensive workshop experiences for undergraduates.

In the next few decades, autonomous vehicles will become an integral part of the traffic flow on highways. However, they will constitute only a small fraction of all vehicles on the road. This research develops technologies to employ autonomous vehicles already in the stream to improve traffic flow of human-controlled vehicles. The goal is to mitigate undesirable jamming, traffic waves, and to ultimately reduce the fuel consumption. Contemporary control of traffic flow, such as ramp metering and variable speed limits, is largely limited to local and highly aggregate approaches. This research represents a step towards global control of traffic using a few autonomous vehicles, and it provides the mathematical, computational, and engineering structure to address and employ these new connections. Even if autonomous vehicles can provide only a small percentage reduction in fuel consumption, this will have a tremendous economic and environmental impact due to the heavy dependence of the transportation system on non-renewable fuels. The project is highly collaborative and interdisciplinary, involving personnel from different disciplines in engineering and mathematics. It includes the training of PhD students and a postdoctoral researcher, and outreach activities to disseminate traffic research to the broader public. This project develops new models, computational methods, software tools, and engineering solutions to employ autonomous vehicles to detect and mitigate traffic events that adversely affect fuel consumption and congestion. The approach is to combine the data measured by autonomous vehicles in the traffic flow, as well as other traffic data, with appropriate macroscopic traffic models to detect and predict congestion trends and events. Based on this information, the loop is closed by carefully following prescribed velocity controllers that are demonstrated to reduce congestion. These controllers require detection and response times that are beyond the limit of a human's ability. The choice of the best control strategy is determined via optimization approaches applied to the multiscale traffic model and suitable fuel consumption estimation. The communication between the autonomous vehicles, combined with the computational and control tasks on each individual vehicle, require a cyber-physical approach to the problem. This research considers new types of traffic models (micro-macro models, network approaches for higher-order models), new control algorithms for traffic flow regulation, and new sensing and control paradigms that are enabled by a small number of controllable systems available in a flow.

This project addresses the foundational problem of knowledge within cyber-physical systems (CPS), i.e., systems that combine aspects such as communication, computation, and physics. A single system observes its environment through sensors and interacts through actuators. Neither is perfect. Thus, the CPS's internal view of the world is blurry and its actions are imprecise. CPS are still analyzed with methods that do not distinguish between truth in the world and an internal view thereof, resulting in a mismatch between the behavior of theoretical models and their real-world counterparts. How could they be trusted to perform safety-critical tasks? This project addresses this critical insufficiency by developing methods to reason about knowledge and learning in CPS. The project pursues the development of new logical principles for verifying knowledge-aware CPS. This project investigates how to make the mismatch between the world and the partial perception through sensors explicit and how to achieve provably correct control in theory as well as practice despite this mismatch. By investigating changing knowledge in a changing world, this project contributes to a fundamental feature without which CPS can never be truly safe and efficient at the same time. The project's broader significance and importance are a result of the widespread attention that CPS gain in many safety-critical areas, such as in aviation and automotive industries. One reason for safety gaps in such CPS is that formal verification techniques are still largely knowledge-agnostic, and verifiable solutions overly pessimistic. This project addresses these issues and provides tools that allow for incorporating knowledge about the environment's intentions into the models to derive provably correct, but justifiably optimistic, and thus efficient, behavior. By their logical nature, these techniques are applicable to a wide range of CPS and, thus, contribute significantly to numerous applications. Results obtained within this project will be demonstrated in CPS models and laboratory robot scenarios, and will be shared in courses and with industrial partners. The technical approach that this project pursues develops a new modeling language, logic, and proof calculus for verifying knowledge-aware CPS. The knowledge paradigm used allows CPS controllers to seamlessly acquire knowledge about the world but also about other agents in the system, i.e., other controllers. Knowledge is the key to interactions between different agents. This project investigates how an explicit model of world perception and agent intentions - and knowledge of these perceptions and intentions - allows CPS agents to act, based on more efficient, but still provably safe control in multi-agent scenarios. The methods will be implemented in the verification tool KeYmaera and demonstrated in formal verification on different case study applications such as car scenarios.

In the next few decades, autonomous vehicles will become an integral part of the traffic flow on highways. However, they will constitute only a small fraction of all vehicles on the road. This research develops technologies to employ autonomous vehicles already in the stream to improve traffic flow of human-controlled vehicles. The goal is to mitigate undesirable jamming, traffic waves, and to ultimately reduce the fuel consumption. Contemporary control of traffic flow, such as ramp metering and variable speed limits, is largely limited to local and highly aggregate approaches. This research represents a step towards global control of traffic using a few autonomous vehicles, and it provides the mathematical, computational, and engineering structure to address and employ these new connections. Even if autonomous vehicles can provide only a small percentage reduction in fuel consumption, this will have a tremendous economic and environmental impact due to the heavy dependence of the transportation system on non-renewable fuels. The project is highly collaborative and interdisciplinary, involving personnel from different disciplines in engineering and mathematics. It includes the training of PhD students and a postdoctoral researcher, and outreach activities to disseminate traffic research to the broader public. This project develops new models, computational methods, software tools, and engineering solutions to employ autonomous vehicles to detect and mitigate traffic events that adversely affect fuel consumption and congestion. The approach is to combine the data measured by autonomous vehicles in the traffic flow, as well as other traffic data, with appropriate macroscopic traffic models to detect and predict congestion trends and events. Based on this information, the loop is closed by carefully following prescribed velocity controllers that are demonstrated to reduce congestion. These controllers require detection and response times that are beyond the limit of a human's ability. The choice of the best control strategy is determined via optimization approaches applied to the multiscale traffic model and suitable fuel consumption estimation. The communication between the autonomous vehicles, combined with the computational and control tasks on each individual vehicle, require a cyber-physical approach to the problem. This research considers new types of traffic models (micro-macro models, network approaches for higher-order models), new control algorithms for traffic flow regulation, and new sensing and control paradigms that are enabled by a small number of controllable systems available in a flow.

In the next few decades, autonomous vehicles will become an integral part of the traffic flow on highways. However, they will constitute only a small fraction of all vehicles on the road. This research develops technologies to employ autonomous vehicles already in the stream to improve traffic flow of human-controlled vehicles. The goal is to mitigate undesirable jamming, traffic waves, and to ultimately reduce the fuel consumption. Contemporary control of traffic flow, such as ramp metering and variable speed limits, is largely limited to local and highly aggregate approaches. This research represents a step towards global control of traffic using a few autonomous vehicles, and it provides the mathematical, computational, and engineering structure to address and employ these new connections. Even if autonomous vehicles can provide only a small percentage reduction in fuel consumption, this will have a tremendous economic and environmental impact due to the heavy dependence of the transportation system on non-renewable fuels. The project is highly collaborative and interdisciplinary, involving personnel from different disciplines in engineering and mathematics. It includes the training of PhD students and a postdoctoral researcher, and outreach activities to disseminate traffic research to the broader public. This project develops new models, computational methods, software tools, and engineering solutions to employ autonomous vehicles to detect and mitigate traffic events that adversely affect fuel consumption and congestion. The approach is to combine the data measured by autonomous vehicles in the traffic flow, as well as other traffic data, with appropriate macroscopic traffic models to detect and predict congestion trends and events. Based on this information, the loop is closed by carefully following prescribed velocity controllers that are demonstrated to reduce congestion. These controllers require detection and response times that are beyond the limit of a human's ability. The choice of the best control strategy is determined via optimization approaches applied to the multiscale traffic model and suitable fuel consumption estimation. The communication between the autonomous vehicles, combined with the computational and control tasks on each individual vehicle, require a cyber-physical approach to the problem. This research considers new types of traffic models (micro-macro models, network approaches for higher-order models), new control algorithms for traffic flow regulation, and new sensing and control paradigms that are enabled by a small number of controllable systems available in a flow.

This project represents a cross-disciplinary collaborative research effort on developing rigorous, closed-loop approaches for designing, simulating, and verifying medical devices. The work will open fundamental new approaches for radically accelerating the pace of medical device innovation, especially in the sphere of cardiac-device design. Specific attention will be devoted to developing advanced formal methods-based approaches for analyzing controller designs for safety and effectiveness; and devising methods for expediting regulatory and other third-party reviews of device designs. The project team includes members with research backgrounds in computer science, electrical engineering, biophysics, and cardiology; the PIs will use a coordinated approach that balances theoretical, experimental and practical concerns to yield results that are intended to transform the practice of device design while also facilitating the translation of new cardiac therapies into practice. The proposed effort will lead to significant advances in the state of the art for system verification and cardiac therapies based on the use of formal methods and closed-loop control and verification. The animating vision for the work is to enable the development of a true in silico design methodology for medical devices that can be used to speed the development of new devices and to provide greater assurance that their behaviors match designers' intentions, and to pass regulatory muster more quickly so that they can be used on patients needing their care. The scientific work being proposed will serve this vision by providing mathematically robust techniques for analyzing and verifying the behavior of medical devices, for modeling and simulating heart dynamics, and for conducting closed-loop verification of proposed therapeutic approaches. The acceleration in medical device innovation achievable as a result of the proposed research will also have long-term and sustained societal benefits, as better diagnostic and therapeutic technologies enter into the practice of medicine more quickly. It will also yield a collection of tools and techniques that will be applicable in the design of other types of devices. Finally, it will contribute to the development of human resources and the further inclusion of under-represented groups via its extensive education and outreach programs, including intensive workshop experiences for undergraduates.

This project represents a cross-disciplinary collaborative research effort on developing rigorous, closed-loop approaches for designing, simulating, and verifying medical devices. The work will open fundamental new approaches for radically accelerating the pace of medical device innovation, especially in the sphere of cardiac-device design. Specific attention will be devoted to developing advanced formal methods-based approaches for analyzing controller designs for safety and effectiveness; and devising methods for expediting regulatory and other third-party reviews of device designs. The project team includes members with research backgrounds in computer science, electrical engineering, biophysics, and cardiology; the PIs will use a coordinated approach that balances theoretical, experimental and practical concerns to yield results that are intended to transform the practice of device design while also facilitating the translation of new cardiac therapies into practice. The proposed effort will lead to significant advances in the state of the art for system verification and cardiac therapies based on the use of formal methods and closed-loop control and verification. The animating vision for the work is to enable the development of a true in silico design methodology for medical devices that can be used to speed the development of new devices and to provide greater assurance that their behaviors match designers' intentions, and to pass regulatory muster more quickly so that they can be used on patients needing their care. The scientific work being proposed will serve this vision by providing mathematically robust techniques for analyzing and verifying the behavior of medical devices, for modeling and simulating heart dynamics, and for conducting closed-loop verification of proposed therapeutic approaches. The acceleration in medical device innovation achievable as a result of the proposed research will also have long-term and sustained societal benefits, as better diagnostic and therapeutic technologies enter into the practice of medicine more quickly. It will also yield a collection of tools and techniques that will be applicable in the design of other types of devices. Finally, it will contribute to the development of human resources and the further inclusion of under-represented groups via its extensive education and outreach programs, including intensive workshop experiences for undergraduates.