CAREER: Cooperative Control Under Communication Constraints

职业：通信限制下的合作控制

• 批准号: 0547199
• 负责人: Sekhar Tatikonda
• 金额: $ 40万
• 依托单位: Yale University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-03-01 至 2012-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0547199&HistoricalAwards=false
• 关键词: CAREER; Cooperative; Control; Under; Communication

CAREER: Cooperative Control Under Communication ConstraintsSummary StatementIntellectual Merit: The fields of cooperative and networked control are on the verge of a technologicalrevolution. Emerging applications in national security, transportation, communication, andcommerce require distributed networks to be capable of multi-user communication, collaborativesignal and information processing, sensor fusion of multi-modal data, and, distributed computation,actuation, and control.These information rich applications place specific demands upon networks: the networks mustscale gracefully to a large numbers of agents; the agents may be geographically distributed, oftenrequiring communication over noisy, bandwidth-limited channels; the networks may consist of heterogeneouscomponents, including embedded systems with limited computational power; and, thenetworks architecture may be decentralized, requiring local coordination amongst the agents.There has been quite a bit of recent activity in exploring graph-based, multi-agent cooperativecontrol. In this proposal the PI will examine how the addition of realistic communication channels,with noise and delay, can effect the cooperative control performance. For noisy channel situationsit is no longer reasonable to assume that each agent has perfect knowledge of its neighbors states.We will examine the question of local versus global knowledge. This project will build on the PIsextensive research on control with communication constraints, graphical models, and informationtheory. In systems with large numbers of weakly coupled agents statistical mechanics tools, likemean-field approximation, can be used to determine the qualitative behavior of the system. Ifthere is stronger coupling between agents then there is the possibility of multiple phases in theseengineered systems.Our main objectives in carrying out this research are: (1) Fundamental limits and tradeoffs.This aspect of the project considers the qualitative scaling behavior of large cooperative controlsystems. In addition, the PI will consider the fundamental limits and tradeoeffs between the qualityof the communication and the resulting control performance. (2)Algorithm development. Theproposed research will develop reinforcement learning techniques for solving large factored Markovdecision problems. (3) Educational development.Broader Impact: In order to develop a useful theory that can explain the advancements incooperative control one needs to view communication and control as two sides of a coin. On theone hand Shannon theory tell us what can be communicated and on the other hand control theorytheory tell us what should be communicated. Tools from information theory, graphical models,probability theory, and statistical mechanics will be used to develop a formal framework for treatingstochastic cooperative control problems. This framework will allow us to realistically model thecommunication between agents and allow us to understand the interaction between informationand control.The proposed research project blends tools from many disciplines. The PI plans to encourageundergraduates, graduate students, and practicing engineers to contribute to this interdisciplinaryresearch program. Course curriculum, talks, and projects will be designed to encourage this participation.To promote a unified view of the theory and algorithms under development and to provide atestbed for the evaluation of ideas we will focus on the following driving applications: distributedestimation and actuation in sensor networks, distributed optimization, multi-agent decision making,message-passing algorithms, and multi-user information theory. Undergraduate and graduatestudents will be called on to perform various experiments and simulations to validate the algorithms.

职业：通信约束下的协同控制概要说明智能优点：协同控制和网络控制领域正处于技术革命的边缘。在国家安全、交通运输、通信和商业等领域的新兴应用要求分布式网络具有多用户通信、协同信号和信息处理、多模态数据的传感器融合以及分布式计算、驱动和控制等能力。代理可能在地理上是分散的，经常需要在嘈杂的、带宽有限的信道上进行通信;网络可能由异构组件组成，包括具有有限计算能力的嵌入式系统;并且，网络架构可以是分散的，需要代理之间的局部协调。最近有相当多的活动在探索基于图的多代理合作控制。在这个建议中，PI将研究如何增加现实的通信信道，噪声和延迟，可以影响合作控制性能。对于有噪声的信道情况，假设每个智能体都完全了解其邻居状态是不合理的，我们将研究局部知识与全局知识的问题。这个项目将建立在PI广泛的研究控制与通信约束，图形模型和信息理论。在具有大量弱耦合代理的系统中，统计力学工具，如平均场近似，可以用来确定系统的定性行为。如果智能体之间有更强的耦合，那么在这些工程系统中就有可能存在多个阶段.我们进行这项研究的主要目标是：（1）基本限制和权衡.这方面的项目考虑了大型合作控制系统的定性标度行为.此外，PI将考虑通信质量和最终控制性能之间的基本限制和权衡。（2）算法开发。建议的研究将发展强化学习技术，解决大因素马尔可夫决策问题。(3)教育发展。更广泛的影响：为了发展一个有用的理论，可以解释合作控制的进步，需要将沟通和控制视为硬币的两面。一方面，香农理论告诉我们什么是可以沟通的，另一方面，控制理论告诉我们什么是应该沟通的.从信息论，图形模型，概率论和统计力学的工具将被用来开发一个正式的框架treatingstochastic合作控制问题。这个框架将使我们能够真实地模拟代理之间的通信，并使我们能够理解信息和控制之间的相互作用。PI计划招募本科生、研究生和实践工程师为这个跨学科研究项目做出贡献。课程设置，讲座和项目将被设计，以鼓励这种participation.To促进一个统一的观点的理论和正在开发的算法，并提供testbed的想法的评估，我们将集中在以下驱动应用：传感器网络，分布式优化，多智能体决策，消息传递算法，和多用户信息理论中的distributedestimation和激励。本科生和研究生将被要求进行各种实验和模拟，以验证算法。