Explanation of Demonstration: The central objective of the proposed research is to transform the CPS-VO from a collaboration platform and passive repository of information into an active resource that provides access to tools and methods emerging from the CPS research community. The project will make a significant contribution to education via support to student competitions that will help prepare a new generation of students who will be inspired and trained to realize the promise of CPS.

The objective of this work is to generate new fundamental science for cyber-physical systems (CPSs) that enables more accurate and faster trajectory synthesis for controllers with nonlinear plants, or nonlinear constraints that encode obstacles. The approach is to utilize hybrid control to switch between models whose accuracy is normalized by their computational burden. This synergistic approach is why we deem our proposed work will enable Computationally Aware Cyber-Physical Systems.

The objective of this research is an injection of new modeling techniques into the area of Cyber-Physical Systems (CPSs). The approach is to design new architectures for domainspecific modeling tools in order to permit feedback from analysis, validation, and verification engines to influence how CPSs are designed. This project outlines new research into the integration of existing, heterogeneous modeling languages in order to address problems in CPS design, rather than a single language used to design any CPS.

Abstract: The Cyber-Physical Systems Virtual Organization (CPS-VO) was founded by NSF in 2010 to: (i) facilitate and foster interaction and exchanges among CPS PIs and their teams; (ii) enable sharing of artifacts and knowledge generated by the projects with the broader engineering and scientific communities; and (iii) facilitate and foster collaboration and information exchange between CPS researchers and industry.

Abstract: This poster summarizes our recently awarded grant on computationally aware cyber-physical systems (CPSs). The objective of this project is to generate new fundamental science for CPSs that enables more accurate and faster trajectory synthesis for controllers with nonlinear plants, or nonlinear constraints that encode obstacles. The approach is to utilize hybrid control to switch between models whose accuracy is normalized by their computational burden.