CAREER: Cooperative Control Under Communication Constraints

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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.