Large battery systems with 100s/1000s cells are being used to power various physical platforms. For example, automobiles are transitioning from conventional powertrains to (plug-in) hybrid and electric vehicles (EVs). To achieve the desired efficiency of EVs, significant improvements are needed in the architecture and algorithms of battery management. This project will develop a new comprehensive battery management architecture, called Smart Battery Management System (SBMS). The research is expected to bridge the wide gap existing between cyber-physical system (CPS) research and electrification industry communities, provide environment-friendly solutions, increase the awareness of CPS, and develop skilled human resources. This project will incorporate and enhance a battery management system (BMS) by including battery state-of-charge (SoC) and state-of-health (SoH) algorithms as well as power management strategies on both pack and cell levels. Specifically, it consists of five main research tasks: (i) design a dynamically reconfigurable energy storage system to tolerate harsh internal and external stresses; (ii) develop cell-level thermal management algorithms; (iii) develop efficient, dependable charge and discharge scheduling algorithms in hybrid energy storage systems; (iv) develop a comprehensive, diagnostic/prognostic (P/D) algorithm with system parameters adjusted for making optimal decisions; and (v) build a testbed and evaluate the proposed architecture and algorithms on the testbed. This research will advance the state-of-the-art in the management of large-scale energy storage systems, extending their life and operation-time significantly, which is key to a wide range of battery-powered physical platforms. That is, SBMS will enable batteries to withstand excessive stresses and power physical platforms for a much longer time, all at low costs. SBMS will also serve as a basic framework for various aspects of CPS research, integrating (cyber) dynamic control and P/D mechanisms, and (physical) energy storage system dynamics.

This project will design next-generation defense mechanisms to protect critical infrastructures, such as power grids, large industrial plants, and water distribution systems. These critical infrastructures are complex primarily due to the integration of cyber and physical components, the presence of high-order behaviors and functions, and an intricate and large interconnection pattern. Malicious attackers can exploit the complexity of the infrastructure, and compromise a system's functionality through cyber attacks (that is hacking into the computation and communication systems) and/or physical attacks (tampering with the actuators, sensors and the control system). This work will develop mathematical models of critical infrastructures and attacks, develop intelligent control-theoretic security mechanisms, and validate the findings on an industry-accredited simulation platform. This project will directly impact national security and economic competitiveness, and the results will be available and useful to utility companies. To accompany the scientific advances, the investigators will also engage in educational efforts to bring this research to the classroom at UCR, and will disseminate their results via scientific publications. The work will also create several opportunities for undergraduate and graduate students to engage in research at UCR, one of the nation's most ethnically diverse research-intensive institutions. This study encompasses theoretical, computational, and experimental research at UCR aimed at characterizing vulnerabilities of complex cyber-physical systems, with a focus on electric power networks, and control-theoretic defense mechanisms to ensure protection and graceful performance degradation against accidental faults and malicious attacks. This project proposes a transformative approach to cyber-physical security, which builds on a unified control-theoretic framework to model cyber-physical systems, attacks, and defense strategies. This project will undertake three main research initiatives ranging from fundamental scientific and engineering research to analysis using industry-accepted simulation tools: (1) modeling and analysis of cyber-physical attacks, and their impact on system stability and performance; (2) design of monitors to reveal and distinguish between accidental and malignant contingencies; and (3) synthesis of adaptive defense strategies for stochastic and highly dynamic cyber-physical systems. Results will first be characterized from a pure control-theoretic perspective with focus on stochastic, switching, and dynamic cyber-physical systems, so as to highlight fundamental research challenges, and then specialized for the case of smart grid, so as to clarify the implementation of monitors, attacks, and defense strategies. The findings and strategies will be validated for the case of power networks by using the RTDS simulation system, which is an industry-accredited tool for real-time tests of dynamic behavior, faults, attacks, monitoring systems, and defensive strategies.

The goal of this project is to establish a theoretical and empirical foundation for secured and efficient energy resource management in the smart grid - a typical energy-based cyber-physical system and the future critical energy infrastructure for the nation. However, as a large distributed and complex system, the smart grid inherently operates under the presence of various uncertainties, which can be raised from natural disasters, malicious attacks, distributed renewable energy resources, plug-in electrical vehicles, habits of energy usage, and weather. These uncertainties make the development of a secured and efficient energy resource management system challenging. To address this challenging problem, a novel modeling framework and techniques to deal with these uncertainties will be developed. Threats and their impact on both system operations and end users will be studied and effective defensive schemes will be developed. The outcomes of this project will have broader impacts on the higher education system and national economy and will provide a scientific foundation for designing a secured and efficient energy-based critical infrastructure. The contributions of this project include: a theoretical framework, techniques, and toolkits for smart grid research and education. Specifically, a modeling framework for secured and efficient energy resource management will be developed to quantify uncertainties from both the cyber and physical power grids. Techniques based on statistical modeling, data mining, forecasting, and others will be developed to manage energy resources efficiently. Based on the developed framework, the space of attacks against system operations and end users from key function modules, attack venue, abilities of adversaries, and system knowledge will be studied systematically. Based on the deep understanding of attacks, novel schemes to prevent, detect, and attribute attacks will be developed. An integrated cyber and physical power grid simulation tool and testbed will be developed to evaluate the proposed modeling framework and techniques using realistic scenarios. This project will integrate research, education, and outreach. The outcomes of the project will be integrated into curriculum development and provide research and educational opportunities for both graduate and undergraduate students, including underrepresented minorities and CyberCorps: Scholarship for Service students.

The objective of this project is to improve the performance of autonomous systems in dynamic environments, such as disaster recovery, by integrating perception, planning paradigms, learning, and databases. For the next generation of autonomous systems to be truly effective in terms of tangible performance improvements (e.g., long-term operations, complex and rapidly changing environments), a new level of intelligence must be attained. This project improves the state of robotic systems by enhancing their ability to coordinate activities (such as searching a disaster zone), recognize objects or people, account for uncertainty, and "most important" learn, so the system's performance is continuously improving. To do this, the project takes an interdisciplinary approach to developing techniques in core areas and at the interface of perception, planning, learning, and databases to achieve robustness. This project seeks to significantly improve the performance of cyber-physical systems for time-critical applications such as disaster monitoring, search and rescue, autonomous navigation, and security and surveillance. It enables the development of techniques and tools to augment all decision making processes and applications which are characterized by continuously changing operating conditions, missions and environments. The project contributes to education and a diverse engineering workforce by training students at the University of California, Riverside, one of the most diverse research institutions in US and an accredited Hispanic Serving Institution. Instruction and research opportunities cross traditional disciplinary boundaries, and the project serves as the basis for undergraduate capstone design projects and a new graduate course. The software and testbeds from this project will be shared with the cyber-physical system research community, industry, and end users. The project plans to present focused workshops/tutorials at major IEEE and ACM conferences. The results will be broadly disseminated through the project website. For further information see the project website at: http://vislab.ucr.edu/RESEARCH/DSLC/DSLC.php

This project focuses on the problem of information acquisition, state estimation and control in the context of cyber physical systems. In our underlying model, a (set of) decision maker(s), by controlling a sequence of actions with uncertain outcomes, dynamically refines the belief about stochastically time-varying parameters of interest. These parameters are then used to control the physical system efficiently and robustly. Here the cyber system collects, processes, and acquires information about the underlying physical system of interest, which is used for its control. The proposed work will develop a new theoretical framework for stochastic learning, decision-making, and control in stochastically-varying cyber physical systems. In order to obtain analytical insights into the structure of efficient design, we first consider the case where the actions of the cyber system only affect the estimate of the underlying physical system. This class of problems arises in the context of (distributed) sensing/tracking of a physical system in isolation from cyber system control of the physical system's state. Joint state estimation and control for cyber-physical systems will then be considered. Here the most natural first step is to obtain sufficient conditions and/or special classes of systems where a separated approach to the information acquisition and efficient control is (near) optimal. To demonstrate its utility in practice, our theoretical framework will be applied in the specific context of energy efficient control of data centers and robust control of the smart grid under limited sensing. The intellectual merit of this work will be to develop a theoretical framework for the design of cyber-physical systems including information acquisition, state estimation, and control. In addition, separation theorems for the optimality of separate state estimation and control will be explored. In terms of broader impacts, significant performance improvement of control systems closed over communication networks will impact a wide range of applications for societal benefit, including smart buildings, intelligent transportation systems, energy-efficient data centers, and the future smart-grid. The PIs plan to disseminate the research results widely through conferences and journals, as well as by organizing specialized workshops and conference sessions related to cyber physical systems. The proposed project will train Ph.D. students as well as enrich the curriculum taught by the PIs in communications, stochastic control, and networks. The PIs have a strong track record in diversity and outreach activities, which for this project will include exposure and involvement of high school and undergraduate students, including under-represented minorities and women.

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

This project establishes a new framework for the formal verification of cyber-physical systems. The framework combines the power of logical decision engines and scalable numerical methods to perform safety verification of general nonlinear hybrid systems. The key difficulty with formal verification of hybrid systems is that all scalable modern verification techniques rely heavily on the use of powerful decision procedures. For hybrid systems, one needs to reason about logic formulas over the real numbers with nonlinear functions, which has been regarded as an intractable problem. The project proposes new directions for tackling the core decision problems, with the combined power of logical and numerical algorithms. The research directly leads to the development of practical tools that will push the frontier of verification of realistic cyber-physical systems to a brand new level. This project aims at fundamental research of problems that stand at the core of the design, analysis, and implementation of reliable cyber-physical systems. It combines techniques from logic, numerical analysis, and automated reasoning, and will produce a unifying methodology that is powerful to address main challenges in this field. The techniques developed in this project will significantly enhance the complexity and reliability of the next generations of cyber-physical systems. Cyber-physical systems are ubiquitous in safety-critical applications as diverse as aerospace, automotive, civil infrastructure, energy, manufacturing, and healthcare. Malfunctioning cyber-physical systems can have catastrophic economic and societal consequences. This project will have a broad range of impact in these areas. This research aims to significantly enhance the management of complexity and reliability of the next generations of cyber-physical systems, and will broadly impact all the application areas.

This project focuses on the problem of information acquisition, state estimation and control in the context of cyber physical systems. In our underlying model, a (set of) decision maker(s), by controlling a sequence of actions with uncertain outcomes, dynamically refines the belief about stochastically time-varying parameters of interest. These parameters are then used to control the physical system efficiently and robustly. Here the cyber system collects, processes, and acquires information about the underlying physical system of interest, which is used for its control. The proposed work will develop a new theoretical framework for stochastic learning, decision-making, and control in stochastically-varying cyber physical systems. In order to obtain analytical insights into the structure of efficient design, we first consider the case where the actions of the cyber system only affect the estimate of the underlying physical system. This class of problems arises in the context of (distributed) sensing/tracking of a physical system in isolation from cyber system control of the physical system's state. Joint state estimation and control for cyber-physical systems will then be considered. Here the most natural first step is to obtain sufficient conditions and/or special classes of systems where a separated approach to the information acquisition and efficient control is (near) optimal. To demonstrate its utility in practice, our theoretical framework will be applied in the specific context of energy efficient control of data centers and robust control of the smart grid under limited sensing. The intellectual merit of this work will be to develop a theoretical framework for the design of cyber-physical systems including information acquisition, state estimation, and control. In addition, separation theorems for the optimality of separate state estimation and control will be explored. In terms of broader impacts, significant performance improvement of control systems closed over communication networks will impact a wide range of applications for societal benefit, including smart buildings, intelligent transportation systems, energy-efficient data centers, and the future smart-grid. The PIs plan to disseminate the research results widely through conferences and journals, as well as by organizing specialized workshops and conference sessions related to cyber physical systems. The proposed project will train Ph.D. students as well as enrich the curriculum taught by the PIs in communications, stochastic control, and networks. The PIs have a strong track record in diversity and outreach activities, which for this project will include exposure and involvement of high school and undergraduate students, including under-represented minorities and women.

The electric power grid is a complex cyber-physical system, whose reliable and secure operation is of paramount importance to national security and economic vitality. There is a growing and evolving threat of cyber-based attacks, both in numbers and sophistication, on the nation's critical infrastructure. Therefore, cyber security "encompassing attack prevention, detection, mitigation, and resilience" is critical in today's power grid and the emerging smart grid. The goal of this project is to develop a unified system-theoretic framework and analytical tools for cyber-physical security of power systems, capturing the dynamics of the physical system as well as that of the cyber system. Research tasks include: 1) Development of a methodology for impact analysis that includes systematic identification of worst-case stealthy attacks on the power system's wide-area control and evaluating the resulting consequences in terms of stability violations and performance loss. 2) Development of robust cyber-physical countermeasures, employing a combination of methods from system theory, cyber security, and model-based/data-driven tools, in the form of domain-specific anomaly detection/tolerance algorithms and attack-resilient control algorithms. 3) Evaluating the effectiveness of the proposed impact modeling and mitigation algorithms through a combination of simulation and testbed-based evaluations, using realistic system topologies and attack scenarios. The project makes significant contributions to enhance the security and resiliency of the power grid and lays a scientific foundation for cyber-physical security of critical infrastructure. Also, the project develops novel curriculum modules, mentors graduate and undergraduate students including under-represented minorities, leverages industrial collaborations, and exposes high school students to cyber security concepts.

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