Design of cyber-physical systems today relies on executable models. Designers develop models, simulate them, find defects, and improve their designs before the system is built, thus greatly reducing the design costs. However, current model-based design methods lack support for model libraries (creating and exchanging models as "black boxes"), tool interoperability (allowing models to be co-simulated by multiple tools), and multi-view modeling (allowing to combine models that "live in different worlds", for instance, a control-logic model with an energy-consumption model). This project seeks to remedy this by developing a compositional modeling framework based on interfaces. Interfaces allow submodels to be treated as black boxes, exposing relevant information while hiding internal details. Success of the project will provide a solid theoretical foundation for compositionality in cyber-physical systems. Compositionality is a key property in system design, allowing to build systems in a scalable and modular manner. This project will enable the construction of model libraries, allowing the exchange of models developed by different teams, potentially coming from different disciplines and using different modeling languages and tools. Besides the considerable economic and societal impact of cyber-physical systems in general, the proposed project will have considerable impact on engineering and computer science education. Its focus on a rigorous and unified modeling theory will erode the boundaries between the currently separated cyber-physical system sub-disciplines that hamper competitiveness of our students. Finally, the project is strategically important for the competitiveness of the United States as it strengthens its presence in international standardization efforts for model exchange and co-simulation.

A hybrid system is a dynamical model that describes the coupled evolution of both continuous-valued variables and discrete patterns. A prime example of such a system is a power electronic circuit, where the semiconductor transistors behave as ideal switches whose switching actions effectively change the circuit topology (i.e., the discrete pattern) that in turn defines the dynamics of currents and voltages (i.e., the continuous variables) and hence the switching actions. There have been two disparate paths to analyzing and designing hybrid systems. One path is to focus on the discrete patterns and achieve scalable, high-level analysis and synthesis. The other path is to pay attention to the dynamics of continuous variables and guarantee low-level properties such as stability and transient performance. The research objective of this proposal is to bridge these approaches by enabling a synergy between the discrete pattern based and continuous variable based approaches. The theory and algorithms developed in course of this work will be applied to digital control of power electronic circuits in order to overcome the scalability and stability issues suffered by existing approaches to power electronics design. The PIs envision that a successful completion of the project will establish a new paradigm in the analysis and design of hybrid systems, and thus contribute to the needs of modern society, such as microgrids and embedded generation, where power electronic circuits are integral parts. The research will be integrated into educational programs through student mentoring and development of courses and laboratory equipment. The PIs will make a special effort to recruit women and minority students. These broader-impact programs will help innovate science and engineering education and prepare for next-generation scientists and engineers.

A hybrid system is a dynamical model that describes the coupled evolution of both continuous-valued variables and discrete patterns. A prime example of such a system is a power electronic circuit, where the semiconductor transistors behave as ideal switches whose switching actions effectively change the circuit topology (i.e., the discrete pattern) that in turn defines the dynamics of currents and voltages (i.e., the continuous variables) and hence the switching actions. There have been two disparate paths to analyzing and designing hybrid systems. One path is to focus on the discrete patterns and achieve scalable, high-level analysis and synthesis. The other path is to pay attention to the dynamics of continuous variables and guarantee low-level properties such as stability and transient performance. The research objective of this proposal is to bridge these approaches by enabling a synergy between the discrete pattern based and continuous variable based approaches. The theory and algorithms developed in course of this work will be applied to digital control of power electronic circuits in order to overcome the scalability and stability issues suffered by existing approaches to power electronics design. The PIs envision that a successful completion of the project will establish a new paradigm in the analysis and design of hybrid systems, and thus contribute to the needs of modern society, such as microgrids and embedded generation, where power electronic circuits are integral parts. The research will be integrated into educational programs through student mentoring and development of courses and laboratory equipment. The PIs will make a special effort to recruit women and minority students. These broader-impact programs will help innovate science and engineering education and prepare for next-generation CPS scientists and engineers.

Intellectual Merit: Recent developments in nanostructures manufacturing, sensing and wireless networking, will soon enable us to deploy Flow-based Cyber-Physical Systems equipped with sensing and actuation capabilities for a broad range of applications. Some of these applications will be safety critical, including water distribution monitoring (i.e., critical national infrastructure systems particularly vulnerable to a variety of attacks, including contamination with deadly agents) and interventional medicine (i.e., a medical branch that makes use of tiny devices introduced in a living body through small incisions, to detect and treat diseases). The goal of this project is to advance our fundamental understanding, through a robust mathematical framework, of emerging field of Flow-based Cyber-Physical System. The project develops new architectures, models, metrics, algorithms and protocols for optimal sensing, communication and actuation in Flow-based Cyber-Physical System deployed on-demand (i.e., reactively, when sensing and actuation is needed) or proactively. Flow-based Cyber Physical Systems consist of mobile sensor nodes and static nodes, aware of their location. For stringent requirements (e.g., form factor, cost, energy budget) nodes may or may not possess node-to-node communication capabilities. Due to the lack of localization infrastructure, mobile sensor nodes infer their location only by proximity to static nodes. Sensor nodes are moved by the flow in the network, detect events of interest and proximity to static nodes, communicate and actuate. This research will enable, for example, water distribution monitoring systems to accurately and timely detect events of interest in the infrastructure and to react to these events. It may enable doctors to detect diseases and deliver medication with microscopic precision. Broader Impacts: Ultimately, the outcomes of this research will have impact on CPS that operate in critical modes and environments and control critical infrastructures and medical applications. The results from this research may also foster new research directions in CPS applications. The PI will integrate the research results in newly approved courses on CPS at Texas A&M and disseminate course materials online through the project website and Rice University Connections Consortium. This project will also offer research opportunities to undergraduate students, underrepresented groups, and high school students participating in the Texas Science Olympiad and National Science Olympiad.

The objective of this research is to develop algorithms and software for treatment planning in intensity modulated radiation therapy under assumption of tumor and healthy organs motion. The current approach to addressing tumor motion in radiation therapy is to treat it as a problem and not as a therapeutic opportunity. However, it is possible that during tumor and healthy organs motion the tumor is better exposed for treatment, allowing for the prescribed dose treatment of the tumor (target) while reducing the exposure of healthy organs to radiation. The approach is to treat tumor and healthy organs motion as an opportunity to improve the treatment outcome, rather than as an obstacle that needs to be overcome. Intellectual Merit: The leading intellectual merit of this proposal is to develop treatment planning and delivery algorithms for motion-optimized intensity modulated radiation therapy that exploit differential organ motion to provide a dose distribution that surpasses the static case. This work will show that the proposed motion-optimized IMRT treatment planning paradigm provides superior dose distributions when compared to current state-of-the art motion management protocols. Broader Impact: Successful completion of the project will mark a major step for clinical applications of intensity modulated radiation therapy and will help to improve the quality of life of many cancer patients. The results could be integrated within existing devices and could be used for training of students and practitioners. The visualization software for dose accumulation could be used to train medical students in radiation therapy treatment planning.

The goal of this research is to develop fundamental theory, efficient algorithms, and realistic experiments for the analysis and design of safety-critical cyber-physical transportation systems with human operators. The research focuses on preventing crashes between automobiles at road intersections, since these account for about 40% of overall vehicle crashes. Specifically, the main objective of this work is to design provably safe driver-assist systems that understand driver's intentions and provide warnings/overrides to prevent collisions. In order to pursue this goal, hybrid automata models for the driver-vehicles-intersection system, incorporating driver behavior and performance as an integral part, are derived from human-factors experiments. A partial order of these hybrid automata models is constructed, according to confidence levels on the model parameters. The driver-assist design problem is then formulated as a set of partially ordered hybrid differential games with imperfect information, in which games are ordered according to parameter confidence levels. The resulting designs are validated experimentally in a driving simulator and in large-scale computer simulations. This research leverages the potential of embedded control and communication technologies to prevent crashes at traffic intersections, by enabling networks of smart vehicles to cooperate with each other, with the surrounding infrastructure, and with their drivers to make transportation safer, more enjoyable, and more efficient. The work is based on a collaboration among researchers in formal methods, autonomous control, and human factors who are studying realistic and provably correct warning/override algorithms that can be readily transitioned to production vehicles.

This project, investigating formal languages as a general methodology for task transfer between distinct cyber-physical systems such as humans and robots, aims to expand the science of cyber physical systems by developing Motion Grammars that will enable task transfer between distinct systems. Formal languages are tools for encoding, describing and transferring structured knowledge. In natural language, the latter process is called communication. Similarly, we will develop a formal language through which arbitrary cyber-physical systems communicate tasks via structured actions. This investigation of Motion Grammars will contribute to the science of human cognition and the engineering of cyber-physical algorithms. By observing human activities during manipulation we will develop a novel class of hybrid control algorithms based on linguistic representations of task execution. These algorithms will broaden the capabilities of man-made systems and provide the infrastructure for motion transfer between humans, robots and broader systems in a generic context. Furthermore, the representation in a rigorous grammatical context will enable formal verification and validation in future work. Broader Impacts: The proposed research has direct applications to new solutions for manufacturing, medical treatments such as surgery, logistics and food processing. In turn, each of these areas has a significant impact on the efficiency and convenience of our daily lives. The PIs serve as coordinators of graduate/undergraduate programs and mentors to community schools. In order to guarantee that women and minorities have a significant role in the research, the PIs will annually invite K-12 students from Atlanta schools with primarily African American populations to the laboratories. One-day robot classes will be conducted that engage students in the excitement of hands-on science by interactively using lab equipment to transfer their manipulation skills to a robot arm.