CPS: Breakthrough: Distributed Computing under Uncertainty: A New Paradigm for Cooperative Cyber-Physical Systems
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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. 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. Toward this end, we have recently developed a new approach to distributed solution of systems of linear equations over unreliable networks. We have proposed a distributed computing system for the Optimal Power Flow problems and analyzed its convergence. Finally, we are developing an optimal and convex control synthesis method for systems over packet drop networks. 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.
Submitted by Nicola Elia
on
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. 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. Toward this end, we have recently developed a new approach to distributed solution of systems of linear equations over unreliable networks. We have proposed a distributed computing system for the Optimal Power Flow problems and analyzed its convergence. Finally, we are developing an optimal and convex control synthesis method for systems over packet drop networks. 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.