Summary Statement Intellectual Merit: The fields of cooperative and networked control are on the verge of a technological revolution. Emerging applications in national security, transportation, communication, and commerce require distributed networks to be capable of multi-user communication, collaborative signal 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 must scale gracefully to a large numbers of agents; the agents may be geographically distributed, often requiring communication over noisy, bandwidth-limited channels; the networks may consist of heterogeneous components, including embedded systems with limited computational power; and, the networks 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 cooperative control. 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 situations it 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 PIs extensive research on control with communication constraints, graphical models, and information theory. In systems with large numbers of weakly coupled agents statistical mechanics tools, like mean-field approximation, can be used to determine the qualitative behavior of the system. If there is stronger coupling between agents then there is the possibility of multiple phases in these engineered 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 control systems. In addition, the PI will consider the fundamental limits and tradeoeffs between the quality of the communication and the resulting control performance. (2)Algorithm development. The proposed research will develop reinforcement learning techniques for solving large factored Markov decision problems. (3) Educational development. Broader Impact: In order to develop a useful theory that can explain the advancements in cooperative control one needs to view communication and control as two sides of a coin. On the one hand Shannon theory tell us what can be communicated and on the other hand control theory theory 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 treating stochastic cooperative control problems. This framework will allow us to realistically model the communication between agents and allow us to understand the interaction between information and control. The proposed research project blends tools from many disciplines. The PI plans to encourage undergraduates, graduate students, and practicing engineers to contribute to this interdisciplinary research 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 a testbed for the evaluation of ideas we will focus on the following driving applications: distributed estimation and actuation in sensor networks, distributed optimization, multi-agent decision making, message-passing algorithms, and multi-user information theory. Undergraduate and graduate students will be called on to perform various experiments and simulations to validate the algorithms.

Project Start
Project End
Budget Start
2006-03-01
Budget End
2012-02-29
Support Year
Fiscal Year
2005
Total Cost
$400,000
Indirect Cost
Name
Yale University
Department
Type
DUNS #
City
New Haven
State
CT
Country
United States
Zip Code
06520