2015 CPS PI MTG Videos, Posters, and Abstracts
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The use of robots in society could be expanded by using reinforcement learning (RL) to allow robots to learn and adapt to new situations on-line. RL is a paradigm for learning sequential decision making tasks, usually formulated as a Markov Decision Process (MDP). For an RL algorithm to be practical for robotic control tasks, it must learn in very few samples, while continually taking actions in real-time. In addition, the algorithm must learn efficiently in the face of noise, sensor/actuator delays, and continuous state features.
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Securing critical networked cyber-physical systems (NCPSs) such as the power grid or transportation systems has emerged as a major national and global priority. The networked nature of such systems renders them vulnerable to a range of attacks both in cyber and physical domains as corroborated by recent threats such as the Stuxnet virus.
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The goal of this research project is to create a scalable and robust cyber-physical system (CPS) framework for the observation and control of the functional interdependencies between bridge structures (stationary physical systems) and trucks, vehicles and human inspectors (mobile physical agents). Figure 1 shows the SHM-based CPS framework that is built around demonstration highway bridges with permanent wireless bridge monitoring system installed.
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This project is developing a formal methods approach to meet temporal logic specifications in traffic control. Formal methods is an area of computer science that develops efficient techniques for proving the correct operation of systems, such as computer programs and digital circuits, and for designing systems that are correct by construction. We have uncovered two key structural properties of traffic networks that make them amenable to this approach.
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Despite the current emphasis on developing green buildings, the global energy consumption issue is not being adequately addressed. In this project, we propose a new perspective on this ever-threatening issue: NetZero energy building clusters.
