CPS: Breakthrough: Distributed Computing Under Uncertainty: A New Paradigm for Cooperative Cyber-Physical Systems

这个项目是开发一个分布式计算的动态系统模型，其灵感来自于最近对平均值的分布式计算的研究。关键思想是静态优化问题（特别是凸优化问题）可以通过设计一个围绕问题的最优解稳定的动态系统来解决。此外，当优化问题可分离时，所设计的动态系统分解为一组局部相互作用的动态系统。这有望为一系列新的计算方法打开大门，这些方法利用了控制工程的最新发展，包括鲁棒控制（为离散化引入的错误提供机制）、马尔可夫跳变线性系统（为随机离散化时间提供机制）、事件驱动控制（为确保异步执行提供机制）、对网络的控制（通常为改进分布式计算系统的性能提供一种机制）。这种新方法在新兴应用程序中是必不可少的，在这些应用程序中，优化运行在物理上分离的代理上，在嘈杂的环境中运行，并且通过不可靠的通道进行通信。作为测试平台，该项目将使用PI开发的两车机器人系统，该系统旨在监测玉米作物，其中该赠款的动态系统视角将允许对最佳站点保持目标进行分布式最佳估计。通过研究自然系统如何集体计算和优化，这项研究有可能影响涉及网络系统的许多学科，从控制电网到对社会、生物或经济系统的行为建模。直接适用于机器人搜救任务、救灾行动、分布式机器学习问题、智能系统等协同网络化多智能体系统。一个有趣的激励应用和理论问题的组合提供了一个独特的多学科的教育机会，学生将参与到项目中，并提供令人兴奋的创新材料的课程和实验室。开发的软件将通过CPS虚拟组织作为开源分发。

This project is to develop a dynamical systems model of distributed computation motivated from recent work on the distributed computation of averages. The key idea is that static optimization problems (particularly convex optimization problems) can be solved by designing a dynamic system that stabilizes around the optimal solution of the problem. Moreover, when the optimization problem is separable, then the designed dynamic system decomposes into a set of locally-interacting dynamic systems. This is expected to open a door to a host of new computational approaches that take advantage of recent developments in control engineering including robust control (providing a mechanism for errors introduced by discretization), Markovian Jump Linear Systems (providing a mechanism for random discretization time), event-driven control (providing a mechanism for assured asynchronous execution), control over networks (providing a mechanism for improved performance of distributed computational systems in general). 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 a two-vehicle robotic system developed by the PI designed to monitor a crop of corn plants, where the dynamic systems perspective of this grant will, for example, allow for distributed optimal estimation toward the goal of optimal station-keeping.By studying how natural systems can collectively compute and optimize, this research has 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. Software developed will be distributed as open source via the CPS Virtual Organization.