Active-passive dynamic consensus filters: Theory and applications

<p>”This dissertation presents a new method for distributively sensing dynamic environments utilizing integral action based system theoretic distributed information fusion methods. Specifically, the main contribution is a new class of dynamic consensus filters, termed active-passive dynamic consensus filters, in which agents are considered to be <i>active</i>, if they are able to sense an exogenous quantity of interest and are considered to be <i>passive</i>, otherwise, where the objective is to drive the states of all agents to the convex hull spanned by the exogenous inputs sensed by active agents. Additionally, we generalize these results to allow agents to locally set their <i>value-of-information</i>, characterizing an agents ability to sense a local quantity of interest, which may change with respect to time.</p><p>The presented active-passive dynamic consensus filters utilize equations of motion in order to diffuse information across the network, requiring continuous information exchange and requiring agents to exchange their measurement and integral action states. Additionally, agents are assumed to be modeled as having single integrator dynamics. Motivated from this standpoint, we utilize the ideas and results from event-triggering control theory to develop a network of agents which only share their measurement state information as required based on errors exceeding a user-defined threshold. We also develop a static output-feedback controller which drives the outputs of a network of agents with general linear time-invariant dynamics to the average of a set of applied exogenous inputs. Finally, we also present a system state emulator based adaptive controller to guarantee that agents will reach a consensus even in the presence of input disturbances.</p><p>For each proposed active-passive dynamic consensus filter, a rigorous analysis of the closed-loop system dynamics is performed to demonstrate stability. Finally, numerical examples and experimental studies are included to demonstrate the efficacy of the proposed information fusion filters”--Abstract, page iv.</p>

Name thesis:degree_name

Ph. D. in Mechanical Engineering

Grantor

Missouri University of Science and Technology

Author dc:creator

• Peterson, John Daniel

• Active Passive Consensus
• Distributed Control
• Dynamic Consensus Filters
• Linear Time Invariant Agents
• Multiagent Control
• Multiagent Systems
• Mechanical Engineering

Repository record dc:identifier

https://scholarsmine.mst.edu/doctoral_dissertations/3108

OAI identifier oai:identifier

oai:scholarsmine.mst.edu:doctoral_dissertations-4113