This award will support a conference for the development of a roadmap for the control community that identifies societal-scale challenges and research paths for addressing them. The roadmap will seek to identify key societal drivers, emerging technological trends, and outline methodologies that squarely address the fundamental challenges by embracing these drivers and leveraging advances in enabling technologies. By doing so, the conference will underscore scientific challenges that the community should pursue, and investigate workforce education and training curricula, with the grand goal of a solid impact by our community on the societal-scale challenges. The funding will be used for travel support for attendees from the United States to participate in the conference and contribute to these deliberations. The 2022 IEEE CSS Workshop on Control for Societal-Scale Challenges (CSS-CSC) is planned to be a decennial event organized by the global control community. The 2022 IEEE CSS Workshop on Control for Societal-Scale Challenges will be an in-person event and will be held in Stockholm, Sweden, June 17-18, 2022. This is intended to be the first of a decennial series of such events, and will be cosponsored by the IEEE Control Systems Society. The main goal of this workshop is to produce a scientific roadmap for the future of our discipline, Control for Societal-Scale Challenges: Roadmap 2030. This roadmap will include themes such as climate change, sharing economy, public health, neuro-engineering, robotics and automation, cyber-physical human systems, smart societies, 6G networks, and Artificial Intelligence. 

The CSS-CSC workshop is being organized by an international committee of scientific excellence, leaders in the control community. Many of the attendees are of equally well-known stature, if not even more so, and have made seminal contributions to the community over the last four decades. This workshop will contribute to the fundamental goal of identifying, underscoring, and advancing potential contributions from the control community for addressing societal-scale challenges that are emerging in the 21st century. This conference is expected to pave the way for a series of such events, with a similar large scope, to occur once every decade. The entire focus of this workshop is in achieving a broader impact on all walks of society including energy, transportation, healthcare, robotics, manufacturing, and overall quality of life. The technologies, the challenges, the opportunities, the methodologies, the tools, the validation testbeds, education, training and retraining of workforce, are the key elements that will be explored in the conference. All in all, every aspect of the proposed conference is intended to achieve a broader impact on the scientific community, and in fact the entire society, as we advance into the 21st century.

This project seeks to develop design methodologies for the synthesis of cyber-physical systems (CPS) that verifiably satisfy given safety and performance requirements when an unknown set of system components is compromised. The need for such design methodologies is exemplified by recent intrusions into nuclear facilities and ransomware attacks on municipal governments, in which adversaries found weak points in cyber defenses that were leveraged to control safety-critical physical infrastructures. The research plan is grounded on two application scenarios: (i) a group of unmanned vehicles that must complete high-level task objectives while avoiding collisions in the presence of false and malicious sensor and control inputs, and (ii) a smart building in which IoT apps send malicious commands to the building HVAC and other safety-critical systems.

The PI will develop algorithms to compute control policies in the presence of attacks that inject arbitrary sensor measurements or control signals, disrupt availability of sensor or control messages, and/or modify controller set points. The first research thrust will investigate and develop control strategies for safety and reachability of nonlinear systems under attack by extending the notions of control barrier and control Lyapunov functions to adversarial settings. The second thrust will investigate resilient synthesis of more complex task specifications using the control algorithms of thrust one as building blocks. The PI will develop novel approaches to model adversarial cyber-physical interactions as stochastic games by developing resilient finite-state abstractions of nonlinear systems. Finite-state control policies will be developed by approximating the game solutions. This thrust will investigate contract-based decomposition algorithms for solving the games in a distributed system with multiple (potentially malicious) decision-making agents. Each thrust of the project will be validated through experimentation and testing on two custom platforms, namely, a multi-robot testbed and a smart building simulation framework. This project will result in models and algorithms to improve safety, performance, and security of CPS including connected and autonomous vehicles, industrial control systems, intelligent traffic management systems, medical devices, and manufacturing CPS. The PI will develop ?serious games? to enhance public interest while providing insight into human decision-making. Algorithms for secure control developed in the project will be experimented on by undergraduate capstone students under the supervision of the PI?s graduate students.

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.

Active user participation in large-scale infrastructure systems, while presenting unprecedented opportunities, also poses significant challenges for the operator. One such example is electric power distribution systems, where the massive integration of distributed energy resources (DERs) and flexible loads motivates new decision-making paradigms via demand response through user engagement. This project introduces a novel approach for intelligent decision making in power distribution systems to efficiently leverage flexible demand commitments in highly uncertain and stochastic environments. The project goals are to (1) develop analytics required to enable actionable demand-side flexibility from several small consumers by adequately representing their constraints regarding electricity usage and their interactions with the system and the energy provider; and (2) develop a prototype for demand-side coordination using an open-source testbed for distribution systems management and evaluate the proposed algorithms with real-world utility data. Successful completion of this project will provide solutions to adaptive and smart infrastructure systems in which passive users turn into active participants. For the demand response focus here, this project will enable high levels of penetration of flexible loads and DERs economically through the transformation of grid operation from load following to supply following. The results from this project will provide valuable guidance to policymakers and electric utilities in managing aggregator-driven markets. 

The central aim of this proposal is to enable the demand-side participation of many small customers in a distribution grid and solve for an interface between customers and an energy provider. The proposed architecture follows a two-level structure: a home energy management system (HEMS) providing a home-level interaction between the consumer and the HEMS, and a feeder-level interaction between the HEMS and the demand-response provider. Research along two thrusts will be proposed: (1) learning-based control to achieve home-level flexibility upon learning and incorporating customer constraints and preferences into the decision-making process; and (2) game-theoretic constructs to aggregate and coordinate the home-level flexibility at the network-level in a constrained environment with unknown customer utility functions. Technical innovations at the HEMS-customer interface will include automata learning-based algorithms used by HEMS to learn customers? temporally evolving energy usage constraints, and reinforcement learning algorithms to satisfy temporal constraints while optimizing the cost of electricity consumption. At the provider-HEMS interface, technical innovations will include a new mean field based model of customers that allows the provider to interact with only a few customer classes, and a Stackelberg game formulation that explicitly incorporates network congestion constraints.

This research examines the scientific foundations for modeling security and privacy trade-offs in cyber-physical systems, focusing in particular on settings where privacy-protection technologies might be abused by malicious parties to hide their attacks. The goal is to provide both security and privacy guarantees for a variety of cyber-physical systems including intelligent transportation systems, smart energy, and autonomous vehicles.

Privacy and security in cyber-physical systems have been studied independently before, but often they have not been addressed jointly. This project will study privacy-protection mechanisms such as differential privacy, and explore how using such mechanisms can affect the state-of-art integrity and attack-detection mechanisms. The project will also develop novel defenses including: 1) Identifying fundamental trade-offs between privacy and security based theoretical analyses of privacy, control theory, and optimization methods, with applications such as traffic-density estimation and smart grids; 2) incorporating game-theoretic considerations in analyzing adversarial strategies; and 3) Proposing new privacy-preserving techniques applicable in cyber-physical systems and beyond.

This project focuses on tackling the security and privacy of Cyber-Physical Systems (CPS) by integrating the theory and best practices from the information security community as well as practical approaches from the control theory community. The first part of the project focuses on security and protection of cyber-physical critical infrastructures such as the power grid, water distribution networks, and transportation networks against computer attacks in order to prevent disruptions that may cause loss of service, infrastructure damage or even loss of life. The second part of the project focuses on privacy of CPS and proposes new algorithms to deal with the unprecedented levels of data collection granularity of physical human activity. The work in these two parts focuses on the integration of practical control theory concepts into computer security solutions. In particular, in the last decade, the control theory community has proposed fundamental advances in CPS security; in parallel, the computer security community has also achieved significant advances in practical implementation aspects for CPS security and privacy. While both of these fields have made significant progress independently, there is still a large language and conceptual barrier between the two fields, and as a result, computer security experts have developed a parallel and independent research agenda from control theory researchers. In order to design future CPS security and privacy mechanisms, the two communities need to come closer together and leverage the insights that each has developed. This project attempts to facilitate the integration of these two communities by leveraging the physical properties of the system under control in two research problems: (1) Physics-based CPS security; and (2) Physics-based CPS privacy.

Physics-based CPS security leverages the time series from sensor and control signals to detect deviations from expected operation. This is a growing area of research in both security and control theory venues, although there are several open problems in this space. This proposal tackles some of these open problems including the definition of new evaluation metrics that capture the unique operational properties of control systems, the consistent evaluation of different proposals for models and anomaly detection tests, and the development of new industrial control protocol parsers. Physics-based CPS privacy focuses on how to guide the implementation of general privacy recommendations like the Fair Information Practice principles into cyber-physical systems, leveraging the fact that these physical systems often have an objective to achieve, and this objective depends on the data-handling policies of the operator. The project focuses on investigating the trade-off between privacy and control performance and developing tools to guide how data minimization, data delays, and data retention should be implemented.

The research goal of this project is to design a comprehensive methodology for cybersecurity monitoring and mitigation of the electric power distribution system with a multitude of dynamical devices that are prone to cyberattacks as well as power electronically interfaced renewable generation units. The power electronics interfaces complicate the control and operation of the distribution system because of their sensitivity to undesired disturbances. By promoting a more secure power system, this work helps increase the resiliency of the distribution system to extreme events, which is in line with the U.S. national priorities. Moreover, this work promotes teaching and learning by (i) educating and training graduate students through several programs including a week-long practicum hosted at an industry site; (ii) disseminating the results of our work via conference presentations and publications; and (iii) introducing significant updates in the syllabi of two courses. This work also increases participation of underrepresented minorities via active recruitment.

The overarching research goal of this project is to design a comprehensive methodology for cybersecurity monitoring and mitigation in systems with a multitude of dynamical devices that are prone to cyberattacks. To demonstrate the performance of our proposed algorithms, we study their application for an electric power distribution system as a critical cyberinfrastructure, which includes substations, feeder devices, and smart meters. The electric power grid's growing dependency on information and communication technology (ICT) significantly increases its vulnerability to cyber attacks. A major area of concern is the security of supervisory control and data acquisition (SCADA) systems that the power grids rely upon for monitoring and control functions. To address these challenges, our specific objectives are (i) detection of cyberthreats on the distribution system, (ii) mitigation of and response to cyberintrusions especially with power electronically interfaced renewables via multiagent-based algorithms as well as trajectory shaping and frequency regulation strategies, and (iii) preparing the next generation of cyber-aware engineers.

This Cyber-Physical Systems (CPS) project will develop advanced artificial intelligence and machine-learning (AI/ML) techniques to harness the extensive untapped climatic resources that exist for direct solar heating, natural ventilation, and radiative and evaporative cooling in buildings. Although these mechanisms for building environment conditioning are colloquially termed "passive," their performance depends strongly on the intelligent control of operable elements such as windows and shading, as well as fans in hybrid systems. Towards this goal, this project will create design methodologies for climate- and occupant-responsive strategies that control these operable elements intelligently in coordination with existing building heating ventilation and air conditioning systems, based on sensor measurements of the indoor and outdoor environments, weather and energy forecasts, occupancy, and occupant preferences. The solutions developed in this project can potentially result in substantial reduction in greenhouse gas emissions generated from space heating, cooling, and ventilation. The developed techniques may be particularly valuable in affordable housing by reducing energy costs under normal conditions and improving passive survivability during extreme events and power outages.

Specifically, this project will create intelligent passive and hybrid conditioning systems that optimally leverage climatic resources in the form of temperate outdoor air and sunlight, harness these resources at the building envelope and redistribute them within the building?s microclimates, and learn to respond to changing weather and evolving occupant needs. The project will advance foundational analysis and design tools for a class of physics-informed machine learning models for systems governed by local energy and mass conservation laws. These so-called locally interactive bilinear ?ow models have broad applicability beyond the specific physical building systems studied in this project. From a fundamental cyber physical systems standpoint, the researchers will establish analytical certificates for learning and control algorithms designed for this class of systems, bridging the gap between purely data-driven strategies and physics-based models. Finally, the project will provide a systematic mechanism to evaluate climate resources available through the intelligent operation of passive systems, bridging a key gap in current understanding. Demonstrations in occupied buildings will provide key insights and evidence to support the applicability of the researched tools in the real world. This effort will also develop and present educational modules to attract middle and high school students to encourage careers in sustainable engineering through the RPI Engineering Ambassadors program; at the same time, project outcomes will also support community engagement with science and technology through the University of Oregon Sustainable City Year program.

This NSF CPS project aims to develop new techniques for modeling cyber-physical systems that will address fundamental challenges associated with scale and complexity in modern engineering. The project will transform human interaction with complex cyber-physical and engineered systems, including critical infrastructure such as interconnected energy networks. This will be achieved through a novel combination of data-driven techniques and physics-based approaches to give mathematical and computational models that are at once abstract enough to be understood by humans making key engineering decisions and precise enough to make quantitative predictions. The intellectual merits of the project include a novel confluence of emerging data science and model-analysis methods, including manifold learning and information geometry. The broader impacts of the project include the training of undergraduates, including those from underrepresented communities, several outreach activities, and publicly available open-source software.

Engineering requirements often make incompatible demands on models. Detailed models make highly accurate predictions, but coarse models are easier to interpret. This project will develop techniques to overcome this inherent contradiction. On the one hand, data science and machine learning techniques allow us to efficiently construct black box predictive models with limited generalizability. At the same time, recent advances in information geometry have produced model reduction methods that systematically derive simple, interpretable models from physical first principles that summarize relevant mechanisms needed for model transferability. Combining these technologies will enable useful mappings between ?physically explainable? reduced models and quantitative data. These data-driven tools will enable ?the best of both worlds? ? physically interpretable models that make quantitative predictions. We will combine a meaningful, qualitatively correct but quantitatively inaccurate reduced model with a data-driven transformation. The project team brings together domain-specific expertise in physical modeling, energy systems, and data-driven learning. We will apply this approach to address key operational challenges in interconnected energy networks. The enabling technology will apply to modeling any complex cyber-physical system.

The application of acoustic monitoring in ecological sciences has grown exponentially in the last two decades. It has been used to answer many questions, including detecting the presence or absence of animal species in an environment, evaluating animal behavior, and identifying ecological stressors and illegal activities. However, current uses are limited to the coverage of relatively small geographic areas with a fixed number of sensors. Animal-borne GPS-based location trackers paired with other sensors are another widely used tool in aiding wildlife conservation and ecosystem monitoring. Since capturing and collaring wild animals is a traumatic event for them, as well as being expensive and resource-intensive, multiyear deployments are required. There are severely limited opportunities to recharge batteries making relatively power-hungry sensing, such as acoustic monitoring, out of reach for existing tracking collars. The aim of the A3EM project is to devise an animal-borne adaptive acoustic monitoring system to enable long-term, real-time observation of the environment and behavior of wildlife. Animal-borne acoustic monitoring will be a novel tool that may provide new insights into biodiversity loss, a severe but underappreciated problem of our time. Combining acoustic monitoring with location tracking collars will enable entirely new applications that will facilitate census gathering and monitoring of threatened and endangered species, detecting poachers of elephants in Africa or caribou in Alaska, and evaluating the effects of mining and logging on wildlife, among many others. All data, hardware designs, and software source code will be released to the public domain, enabling tracking collar manufacturers to include the technology within their products. A3EM constitutes a complex cyber-physical architecture involving humans, animals, distributed sensing devices, intelligent environmental monitoring agents, and limited power and network connectivity. This intermittently connected CPS, with a power budget an order of magnitude lower than typical, calls for novel approaches with a high level of autonomy and adaptation to the physical environment. A3EM will employ a unique combination of supervised and semi-supervised embedded machine learning to identify new and unexplored event classes in a given environment, dynamically control and adjust parameters related to data acquisition and storage, opportunistically share knowledge and data between distributed sensing devices, and optimize the management of storage and communication to minimize resource needs. These methods will be evaluated through the creation of a wearable acoustic monitoring system used to support ecological applications such as enhanced wildlife protection, rare species identification, and human impact studies on animal behavior.