CAREER: Algorithms and Fundamental Limitations for Sparse Control

职业：稀疏控制的算法和基本限制

• 批准号: 1351684
• 负责人: Alexander Olshevsky
• 金额: $ 40万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-03-01 至 2017-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1351684&HistoricalAwards=false
• 关键词: CAREER; Algorithms; Fundamental; Limitations; Sparse

The proposal is to study the design of feedback control strategies which stabilize and steer systems by affecting them in only a few variables. The motivation comes from applications which are either large-scale or geographically distributed and therefore cannot be feasibly affected in many places. A primary motivating application is the control of metabolic chemical reaction networks within the human body which can be affected by drugs typically interacting with only a few out of the tens of thousands reagents in the human metabolism. The goal is to design sparse strategies which stabilize models of metabolic networks away from undesirable equilibria with an eye to developing algorithms which could one day be used to design drugs regulating human metabolism.Intellectual Merit:The design of efficient algorithms which find the sparsest possible controllers for linear and polynomial dynamical systems will be investigated. Whenever this is not possible intractability results rigorously demonstrating this impossibility will be developed. A central focus of the work will be on computational complexity issues as the search for sparse controllers turns out to be intractable in many cases of interest. The main contribution will be in the development of algorithms which take advantage of the generic properties of real-world systems to avoid intractability barriers and efficiently find very sparse controllers.Broader Impacts:The algorithms have potential to become standard tools of control engineering practice whenever large systems are involved or when the number of sensors and actuators available is limited. The PI will work to ensure that the protocols developed here enter into the control curriculum. Both undergraduate and graduate students will be involved in the execution of the research. Outreach activities are planned, especially for beginning undergraduate students with the aim of increasing retention rates of under-represented groups in engineering.

该提案旨在研究反馈控制策略的设计，该策略通过仅影响少数变量来稳定和引导系统。其动机来自于大规模或地理分布的应用程序，因此在许多地方都不会受到影响。一个主要的应用是控制人体内的代谢化学反应网络，该网络可能会受到药物的影响，这些药物通常只与人体代谢中数以万计的试剂中的少数几种相互作用。目标是设计稀疏策略，稳定代谢网络模型，使其远离不良平衡，着眼于开发算法，有一天可以用于设计调节人类新陈代谢的药物。智力优点：设计有效的算法，找到最稀疏的可能控制器将研究线性和多项式动力系统。只要这是不可能的，就会产生严格证明这种不可能性的棘手结果。这项工作的中心焦点将是计算复杂性问题，因为在许多感兴趣的情况下，搜索稀疏控制器是很棘手的。主要贡献将在于开发算法，该算法利用现实世界系统的通用属性来避免棘手的障碍并有效地找到非常稀疏的控制器。更广泛的影响：每当规模较大时，算法就有可能成为控制工程实践的标准工具涉及系统或可用传感器和执行器数量有限时。 PI 将努力确保此处开发的协议进入控制课程。本科生和研究生都将参与研究的执行。计划开展外展活动，特别是针对本科生，旨在提高工程领域代表性不足群体的保留率。