CPS: Breakthrough: Distributed computing under uncertainty: a new paradigm for cooperative cyber-physical systems

Abstract:

This project is to develop dynamical models of distributed computation systems that are resilient to noise, unreliable communication and other source uncertainty. The key idea is centered on the development of optimization systems. These are dynamical systems that (solve) stabilize around the optimal solution of a (static) convex optimization problem. If the optimization problem is separable, then the designed dynamic system decomposes into a set of locally interacting dynamic systems. This is expected to open the door to a host of new computational approaches that take advantage of recent developments in control engineering including robust control, Markovian Jump Linear Systems, event--driven control and control over networks. The new approach is essential in emerging applications, where the optimization runs on physically separated agents, operating in a noisy environment and communicating over unreliable channels. As a test bed, the project will make use of cooperative aerial and ground robots where the proposed dynamic systems perspective will provide distributed optimal estimation and positioning methods for precise cooperative sensing.By studying how natural systems can collectively compute and optimize, this research has the potential to impact many disciplines involving networked systems, from controlling the electric power grid, to modeling the behavior of social, biological or economic systems. It is directly applicable to cooperative networked multi--agent systems like robotic search and rescue missions and disaster--relief operations, distributed machine learning problems, and intelligent systems. An intriguing mix of motivating applications and theoretical problems offer a unique multidisciplinary educational opportunity to students who will be involved in the project, and provide exciting innovative material for courses and labs.