NSF CPS: Synergy: Collaborative Research: Computationally Aware Cyber-Physical Systems
PIs: Ricardo Sanfelice and Jonathan Sprinkle.
Campus: UC Santa Cruz
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 results from this project will advance the knowledge on modeling, analysis, and design of CPSs that utilize predictive methods for trajectory synthesis under constraints. Current algorithm designs seldom include the computational limitations of the hardware/software on which they are implemented as explicit constraints; thus, a challenge is to correctly approximate (or account for) how these constraints can be overcome for real-time systems. The results will include methods for the design of algorithms that adapt to the computational limitations of autonomous and semi-autonomous systems that must satisfy stringent timing and safety requirements. For this purpose, we propose tools capable of accounting for computational capabilities in real-time, and hybrid feedback algorithms that include prediction schemes exploiting computational capabilities to arrive at more accurate predictions, within the time constraints. The problem space will draw from models of Unmanned Air Systems (UAS) in the National Air Space (NAS); algorithms will be modeled in terms of hybrid dynamical systems, to guarantee dynamical properties of interest. More info: https://hybrid.soe.ucsc.edu/projects
AFOSR: Reconfigurable Algorithms for High Performance and Robust Autonomy in Complex Networks
PIs: Ricardo Sanfelice
Campus: UC Santa Cruz
This project aims to generate new tools for the design of decentralized observers and controllers that not only confer high performance and robustness, but also efficiently handle the presence of continuous and discrete behavior in complex networks. The methods to emerge from the proposed research target communication and control scenarios for Air Force systems in which digital networks define the links between the agents, information is limited, adversarial behavior is at its utmost, and uncertainty is predominant. The algorithms to be developed with the new methods will combine hybrid estimation strategies to identify the behavior of the adversaries with hybrid control strategies for synchronization of multi-agent systems so as to reconfigure and control the agents. The fundamental research in this proposal pertains to the design of hybrid state observers and synchronization algorithms for hybrid systems, which are wide open areas of research. The outcomes of this research effort will permit Air Force’s autonomous systems to satisfy the stringent requirements imposed by its missions, in particular, the accomplishment of a common goal by relying only on noisy and intermittent measurements transmitted over heterogeneous networks. More info: https://hybrid.soe.ucsc.edu/projects