During the last decade, we have witnessed a rapid penetration of autonomous systems technology into aerial, road, underwater, and sea vehicles. The autonomy assumed by these vehicles holds the potential to increase performance significantly, for instance, by reducing delays and increasing capacity, while enhancing safety, in a number of transportation systems. However, to exploit the full potential of these autonomy-enabled transportation systems, we must rethink transportation networks and control algorithms that coordinate autonomous vehicles operating on such networks. This project focuses on the design and operation of autonomy-enabled transportation networks that provide provable guarantees on achieving high performance and maintaining safety at all times. The foundational problems arising in this domain involve taking into account the physics governing the vehicles in order to coordinate them using cyber means. This research effort aims to advance the science of cyber-physical systems by following a unique and radical approach, drawing inspiration and techniques from non-equilibrium statistical mechanics and self-organizing systems, and blending this inspiration with the foundational tools of queueing theory, control theory, and optimization. This approach may allow orders of magnitude improvement in the servicing capabilities of various transportation networks for moving goods or people. The applications include the automation of warehouses, factory floors, sea ports, aircraft carrier decks, transportation networks involving driverless cars, drone-enabled delivery networks, air traffic management, and military logistics networks. The project also aims to start a new wave of classes and tutorials that will create trained engineers and a research community in the area of safe and efficient transportation networks enabled by autonomous cyber-physical systems.

Epilepsy is one of the most common neurological disorders, affecting between 0.4% and 1% of the world's population. While seizures can be controlled in approximately two thirds of newly diagnosed patients through the use of one or more antiepileptic drugs (AEDs), the remainder experience seizures even on multiple medications. The primary impacts of the chronic condition of epilepsy on a patient are a lower quality of life, loss of productivity, comorbidities, and increased risk of death. Epilepsy is an intermittent brain disorder, and in localization-related epilepsy, which is the most common form of epilepsy, one or a few discrete brain areas (the seizure focus or seizure foci) are believed to be responsible for seizure initiation. More recent approaches with implantable electrical stimulation seizure control devices hold value as a therapeutic option for the control of seizures. These devices, directly or indirectly, target the seizure focus and seek to control its expression. In this project we will build a multichannel brain implantable device based on emerging cyber physical system (CPS) principles. This brain implantable CPS device will incorporate key design features to make the device dependable, scalable, composable, certifiable, and interoperable. The device will operate over the life of an animal, or a patient, and continuously record brain activity and stimulate the brain when seizure related activity is detected to abort an impending seizure. Episodic brain disorders such as epilepsy have a considerable impact on a patient's productivity and quality of life and may be life-threatening when seizures cannot be controlled with medications. The goal of this project is to create a second generation brain-implantable sensing and stimulating device (BISSD) based on emerging CPS principles and practice. The development of a BISSD as a exemplifies several defining aspects that inform and illustrate core CPS principles. First, to meet the important challenge of regulatory approval a composable, scalable and certifiable framework that supports testing in multiple species is proposed. Second, a BISSD must be wholly integrated with the patient and fully cognizant at every instant of brain state, including dynamic changes in both the normal and abnormal expression of brain physiology and therapeutic intervention. Thus, this project seeks a tight conjunction of the cyber solution that must monitor itself and monitor and stimulate the brain using implanted, adaptable, distributed, and networked electrodes, and the physical system which in this case is the intermittently failing human brain. Third, a BISSD must function for an extensive period of time, up to the life of the patient, because each surgery to place and retrieve a BISSD carries an attendant risk. This requirement necessitates a dependable solution, which this project seeks to reliably achieve through both an understanding of the brain's foreign body response and a unique hierarchical fault-tolerant design. Fourth, an advanced salient approaches to acquire, compress, and analyze sensor signals to achieve real-time monitoring and control of seizures is employed. This project should yield a powerful, scalable CPS framework for robust fault-tolerant implantable medical devices with real-time processing that can grow with advances in sensors, sensing modalities, time-series analysis, real-time computation, control, materials, power and knowledge of underlying biology. The USA has a competitive advantage in the control of seizures in medically refractory epilepsy. In the modern era, epilepsy surgery evolved in the USA in the 1970s and spread from here to other parts of the world. Similarly, the USA enjoys a competitive advantage in BISSDs, and success in this effort will enable the USA to build on and maintain this advantage. In addition to epilepsy, advances made here can be expected to benefit the treatment of other neurological and psychiatric brain disorders.

The objective of this project is to develop optimization and control techniques and integrate them with real-time simulation models to achieve load balancing in complex networks. The application case is the regional freight system. Freight moves on rail and road networks which are also shared by passengers. These networks today work independently, even though they are highly interdependent, and the result is inefficiencies in the form of congestion, pollution, and excess fuel consumption. These inefficiencies are observed for example by the peaks of demand across time and space. Inefficiencies exist in part due to lack of information and appropriate tools, and in part due to lack of policies and institutional structures that would promote more integrated operations. The problem is made even more complex due to the large quantities of real time data that will be available to inform the decision-making. This research develops the theoretical foundations of a new approach referred to as COSMO to balance loads across complex dynamical networks with temporal and spacial characteristics. In contrast to current practices where simple mathematical models are used to predict the states of the network the method employs computational simulation models that are far more accurate in estimating the states of the network by taking into account dynamics and complex interactions. The project develops the optimization and load balancing control segments of the cyber physical system and integrate them with real time network simulation models using freight transportation as the driving application area. The research also examines how identified barriers and policy issues/incentives can be incorporated as mathematical constraints and/or control variables in the optimized dynamic freight load balancing system. Data supporting the analysis may include freight characterization, traffic, weather, and other large data volumes. The project will utilize real time data from the port of Los Angeles /Long Beach area to validate the approach. The port of Los Angeles/Long Beach is the port of entry for much of the freight that enters the West Coast, and provides rich sets of data that will stimulate the model especially in regional transportation involving interaction between road/rail/port networks. This fundamental technology in this important transportation domain with direct applications to other large scale freight centers, can be applied to other application domains including networking and smart grid. Besides the broader impact derived from more efficient allocation of transportation resources, the project also provide educational outreach and produce course modules.

The wide-area measurement systems technology using Phasor Measurement Units (PMUs) has been regarded as the key to guaranteeing stability, reliability, state estimation, and control of next-generation power systems. However, with the exponentially increasing number of PMUs, and the resulting explosion in data volume, the design and deployment of an efficient wide-area communication and computing infrastructure is evolving as one of the greatest challenges to the power system and IT communities. The goal of this NSF CPS project is to address this challenge, and construct a massively deployable cyber-physical architecture for wide-area control that is fast, resilient and cost-optimal (FRESCO). The FRESCO grid will consist of a suite of optimal control algorithms for damping oscillations in power flows and voltages, implemented on top of a cost-effective and cyber-secure distributed computing infrastructure connected by high-speed wide-area networks that are dynamically programmable and reconfigurable. The value of constructing FRESCO is twofold (1) If a US-wide communication network capable of transporting gigabit volumes of PMU data for wide-area control indeed needs to be implemented over the next five years then power system operators must have a clear sense of how various forms of delays, packet losses, and security threats affect the stability of these control loops. (2) Moreover, such wide-area communication must be made economically feasible and sustainable via joint decision-making processes between participating utility companies, and testing how controls can play a potential role in facilitating such economics. Currently, there is very limited insight into how the PMU data transport protocols may lead to a variety of such delay patterns, or dictate the economic investments. FRESCO will answer all of these questions, starting from small prototypical grid models to those with tens of thousands of buses. Our eventual goal will be to make FRESCO fully open-source for Transition to Practice (TTP). We will work with two local software companies in Raleigh, namely Green Energy Corporation and Real-Time Innovations, Inc. to develop a scalable, secure middleware using Data-Distribution Service (DDS) technology. Thus, within the scope of the project, we also expect to enrich the state-of-the-art cloud computing and networking technologies with new control and management functions. From a technical perspective, FRESCO will answer three main research questions. First, can wide-area controllers be co-designed in sync with communication delays to make the closed-loop system resilient and delay-aware, rather than just delay-tolerant This is particularly important, as PMU data, in most practical scenarios, will have to be transported over a shared resource, sharing bandwidth with other ongoing applications, giving rise to not only transport delays, but also significant delays due to queuing and routing. Advanced ideas of arbitrated network control designs will be used to address this problem. The second question we address is for cost. Given that there are several participants in this wide-area control, how much is each participant willing to pay in sharing the network cost with others for the sake of supporting a system-wide control objective compared to its current practice of opting for selfish feedback control only Ideas from cooperative game theory will be used to investigate this problem. The final question addresses security how can one develop a scientific methodology to assess risks, and mitigate security attacks in wide-area control? Statistical and structural analysis of attack defense modes using Bayesian and Markov models, game theory, and discrete-event simulation will be used to address this issue. Experimental demos will be carried out using the DETER-WAMS network, showcasing the importance of cyber-innovation for the sustainability of energy infrastructures. Research results will be broadcast through journal publications, and jointly organized graduate courses between NCSU, MIT and USC.

Inherent vulnerabilities of information and communication technology systems to cyber-attacks (e.g., malware) impose significant security risks to Cyber-Physical Systems (CPS). This is evidenced by a number of recent accidents. Noticeably, current distributed control of CPS is not really attack-resilient (ensuring task completion despite attacks). Although provable resilience would significantly lift the trustworthiness of CPS, existing defenses are rather ad-hoc and mainly focus on attack detection. In addition, while network attacks have been extensively studied, resilient-to-malware distributed control has been rarely investigated. This project aims to bridge the gap. It aims to investigate provably correct distributed attack-resilient control of CPS. The project will focus on a representative class of CPS, namely unmanned-vehicle-operator networks, and its four main research thrusts are: (1) The development of a distributed attack-resilient control framework to ensure task completion of multiple vehicles despite network attacks and malware attacks, (2) The synthesis of novel distributed attack-resilient control algorithms to deal with network attacks, (3) The design of estimation algorithms to detect malware attacks on vehicles, and computationally efficient algorithms which allow clean vehicles to avoid the collision with the vehicles compromised by malware, and (4) The validation of the cost-effectiveness of the proposed distributed attack-resilient control framework via a principled systematic evaluation plan. The research findings profoundly impact CPS security of a variety of engineering disciplines beyond unmanned-vehicle-operator networks, including smart grid, smart buildings and intelligent transportation systems. The proposed research is interdisciplinary and involves interactions among security, control, distributed algorithms and robotics. This will lead to educational and training opportunities that cross traditional disciplinary boundaries for high-school, undergraduate and graduate students in STEM.

The increasing reliance on computer and communication technologies exposes control systems to cyber security threats. The physical systems can now be attacked through cyberspace. Emerging sophisticated attacks can exploit zero-day vulnerabilities, persist in the system for long periods of time, and advance stealthily to achieve their attack goals. Protection and prevention against such attacks are not always possible, and a paradigm shift to emphasize resilience of a control system is the overarching objective for safeguarding control systems to protect nation's critical infrastructures. The major challenge for designing secure and resilient cyber-physical control system is the lack of scientific foundations, and quantitative methods to provide a systematic guideline for large-scale cyber-physical interactions. To this end, the project aims to establish a meta-game system theory, and develop computational and design methodologies for cyber-physical co-design problems. Game-theoretic tools serve as an appropriate way to interconnect systems from multiple domains into one single framework to address security and resilience issues of highly integrated CPS. This project investigates a meta-game framework as a new paradigm to compose heterogeneous system components to design their interactions to achieve functional security and resiliency properties. Through developing security-aware controllers and impact-aware proactive cyber defense mechanism, this project creates a system co-design paradigm based on the meta-game framework, which captures the system properties of robustness, security, and resilience in one single framework, and provides fundamental principles to characterize their tradeoffs. The analytical framework will lead to the development of a cyber-physical mechanism design theory to provide a solid foundation for achieving optimal cyber-physical integration for control systems. The developed analytical and design tools will allow the prediction of unexpected outcomes of system integrations, the mitigation of the impact of cyber attacks on control systems, and the cost-effective operation and design of resilient CPS.

This project develops advanced cyber-physical sensing, modeling, control, and optimization methods to significantly improve the efficiency of algal biomass production using membrane bioreactor technologies for waste water processing and algal biofuel. Currently, many wastewater treatment plants are discharging treated wastewater containing significant amounts of nutrients, such as nitrogen, ammonium, and phosphate ions, directly into the water system, posing significant threats to the environment. Large-scale algae production represents one of the most promising and attractive solutions for simultaneous wastewater treatment and biofuel production. The critical bottleneck is low algae productivity and high biofuel production cost. The previous work of this research team has successfully developed an algae membrane bioreactor (A-MBR) technology for high-density algae production which doubles the productivity in an indoor bench-scale environment. The goal of this project is to explore advanced cyber-physical sensing, modeling, control, and optimization methods and co-design of the A-MBR system to bring the new algae production technology into the field. The specific goal is to increase the algal biomass productivity in current practice by three times in the field environment while minimizing land, capital, and operating costs. Specifically, the project will (1) adapt the A-MBR design to address unique new challenges for algae cultivation in field environments, (2) develop a multi-modality sensor network for real-time in-situ monitoring of key environmental variables for algae growth, (3) develop data-driven knowledge-based kinetic models for algae growth and automated methods for model calibration and verification using the real-time sensor network data, and (4) deploy the proposed CPS system and technologies in the field for performance evaluations and demonstrate its potentials. This project will demonstrate a new pathway toward green and sustainable algae cultivation and biofuel production using wastewater, addressing two important challenging issues faced by our nation and the world: wastewater treatment and renewable energy. It will provide unique and exciting opportunities for mentoring graduate students with interdisciplinary training opportunities, involving K-12 students, women and minority students. With web-based access and control, this project will convert the bench-scale and pilot scale algae cultivation systems into an exciting interactive online learning platform to educate undergraduate and high-school students about cyber-physical system design, process control, and renewable biofuel production.

Infrastructure networks are the foundation of the modern world. Their continued reliable and efficient function without exhausting finite natural resources is critical to the security, continued growth and technological advancement of the United States. Currently these systems are in a state of rapid flux due to a collision of trends such as growing populations, expanding integration of information technology, and increasing motivation to adopt sustainable practices. These trends beget both exciting potential benefits and dangerous challenges. Added sensing, communication, and computational capabilities hold the promise of increased reliability, efficiency and sustainability from "smart" infrastructure systems. At the same time, new technologies such as renewable energy resources in power systems, autonomous vehicles, and software defined communication networks, are testing the limits of current operational and market policies. The rapidly changing suite of system components can cause new, unforeseen interactions that can lead to instability, performance deterioration, or catastrophic failures. Achieving the full benefits of these systems will require a shift from the existing focus on approaches that analyze each aspect of interest in isolation, to a more holistic view that encompasses all of the relevant factors such as stability, robustness, performance and efficiency, and takes into account the presence of human participants. This project provides a research roadmap to construct analysis, design and control tools that ensure the seamless integration of computational algorithms, physical components and human interactions in next generation infrastructure systems. Although there has been a great deal of research on stability questions in large scale distributed systems, there has been little effort directed toward questions of performance, robustness and efficiency in these systems, especially those with heterogeneous components and human participants. This research employs coupled oscillator systems as a common modeling framework to (i) characterize stability and performance of infrastructure systems, and (ii) develop distributed controllers that guarantee performance, efficiency and robustness by isolating disturbances and optimizing performance objectives. Practical solutions require that the theory be tightly integrated with the economic mechanisms necessary to incentivize users to enhance system stability, efficiency and reliability; therefore the work will also include the design of economic controls. In order to ground the mathematical foundations, theory and algorithms described above, the results will be applied to three target infrastructure networks where coupled oscillator models have played a foundational role in design and control: power, communication, and transportation systems. This approach allows the development of cross-cutting, fundamental principles that can be applied across problem specific boundaries and ensures that the research makes an impact on these specific infrastructure networks. This project will also incorporate concepts into existing undergraduate and graduate courses.

Cyber-physical systems of the near future will collaborate with humans. Such cognitive systems will need to understand what the humans are doing. They will need to interpret human action in real-time and predict the humans' immediate intention in complex, noisy and cluttered environments. This proposal puts forward a new architecture for cognitive cyber-physical systems that can understand complex human activities, and focuses specifically on manipulation activities. The proposed architecture, motivated by biological perception and control, consists of three layers. At the bottom layer are vision processes that detect, recognize and track humans, their body parts, objects, tools, and object geometry. The middle layer contains symbolic models of the human activity, and it assembles through a grammatical description the recognized signal components of the previous layer into a representation of the ongoing activity. Finally, at the top layer is the cognitive control, which decides which parts of the scene will be processed next and which algorithms will be applied where. It modulates the vision processes by fetching additional knowledge when needed, and directs the attention by controlling the active vision system to direct its sensors to specific places. Thus, the bottom layer is the perception, the middle layer is the cognition, and the top layer is the control. All layers have access to a knowledge base, built in offline processes, which contains the semantics about the actions. The feasibility of the approach will be demonstrated through the development of a smart manufacturing system, called MONA LISA, which assists humans in assembly tasks. This system will monitor humans as they perform assembly task. It will recognize the assembly action and determine whether it is correct and will communicate to the human possible errors and suggest ways to proceed. The system will have advanced visual sensing and perception; action understanding grounded in robotics and human studies; semantic and procedural-like memory and reasoning, and a control module linking high-level reasoning and low-level perception for real time, reactive and proactive engagement with the human assembler. The proposed work will bring new tools and methodology to the areas of sensor networks and robotics and is applicable, besides smart manufacturing, to a large variety of sectors and applications. Being able to analyze human behavior using vision sensors will have impact on many sectors, ranging from healthcare and advanced driver assistance to human robot collaboration. The project will also catalyze K-12 outreach, new courseware (undergraduate and graduate), publication and open-source software.

Part 1: Upper-limb motor impairments arise from a wide range of clinical conditions including amputations, spinal cord injury, or stroke. Addressing lost hand function, therefore, is a major focus of rehabilitation interventions; and research in robotic hands and hand exoskeletons aimed at restoring fine motor control functions gained significant speed recently. Integration of these robots with neural control mechanisms is also an ongoing research direction. We will develop prosthetic and wearable hands controlled via nested control that seamlessly blends neural control based on human brain activity and dynamic control based on sensors on robots. These Hand Augmentation using Nested Decision (HAND) systems will also provide rudimentary tactile feedback to the user. The HAND design framework will contribute to the assistive and augmentative robotics field. The resulting technology will improve the quality of life for individuals with lost limb function. The project will help train engineers skilled in addressing multidisciplinary challenges. Through outreach activities, STEM careers will be promoted at the K-12 level, individuals from underrepresented groups in engineering will be recruited to engage in this research project, which will contribute to the diversity of the STEM workforce. Part 2: The team previously introduced the concept of human-in-the-loop cyber-physical systems (HILCPS). Using the HILCPS hardware-software co-design and automatic synthesis infrastructure, we will develop prosthetic and wearable HAND systems that are robust to uncertainty in human intent inference from physiological signals. One challenge arises from the fact that the human and the cyber system jointly operate on the same physical element. Synthesis of networked real-time applications from algorithm design environments poses a framework challenge. These will be addressed by a tightly coupled optimal nested control strategy that relies on EEG-EMG-context fusion for human intent inference. Custom distributed embedded computational and robotic platforms will be built and iteratively refined. This work will enhance the HILCPS design framework, while simultaneously making novel contributions to body/brain interface technology and assistive/augmentative robot technology. Specifically we will (1) develop a theoretical EEG-EMG-context fusion framework for agile HILCPS application domains; (2) develop theory for and design novel control theoretic solutions to handle uncertainty, blend motion/force planning with high-level human intent and ambient intelligence to robustly execute daily manipulation activities; (3) further develop and refine the HILCPS domain-specific design framework to enable rapid deployment of HILCPS algorithms onto distributed embedded systems, empowering a new class of real-time algorithms that achieve distributed embedded sensing, analysis, and decision making; (4) develop new paradigms to replace, retrain or augment hand function via the prosthetic/wearable HAND by optimizing performance on a subject-by-subject basis.