OBSERVABILITY-BASED SAMPLING AND ESTIMATION OF FLOWFIELDS USING MULTI-SENSOR SYSTEMS

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dc.contributor.advisorPaley, Derek Aen_US
dc.contributor.authorDeVries, Levi Daviden_US
dc.contributor.departmentAerospace Engineeringen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2014-10-17T05:32:30Z
dc.date.available2014-10-17T05:32:30Z
dc.date.issued2014en_US
dc.description.abstractThe long-term goal of this research is to optimize estimation of an unknown flowfield using an autonomous multi-vehicle or multi-sensor system. The specific research objective is to provide theoretically justified, nonlinear control, estimation, and optimization techniques enabling a group of sensors to coordinate their motion to target measurements that improve observability of the surrounding environment, even when the environment is unknown. Measures of observability provide an optimization metric for multi-agent control algorithms that avoid spatial regions of the domain prone to degraded or ill-conditioned estimation performance, thereby improving closed-loop control performance when estimated quantities are used in feedback control. The control, estimation, and optimization framework is applied to three applications of multi-agent flowfield sensing including (1) environmental sampling of strong flowfields using multiple autonomous unmanned vehicles, (2) wake sensing and observability-based optimal control for two-aircraft formation flight, and (3) bio-inspired flow sensing and control of an autonomous unmanned underwater vehicle. For environmental sampling, this dissertation presents an adaptive sampling algorithm steering a multi-vehicle system to sampling formations that improve flowfield observability while simultaneously estimating the flow for use in feedback control, even in strong flows where vehicle motion is hindered. The resulting closed-loop trajectories provide more informative measurements, improving estimation performance. For formation flight, this dissertation uses lifting-line theory to represent a two-aircraft formation and derives optimal control strategies steering the follower aircraft to a desired position relative to the leader while simultaneously optimizing the observability of the leader's relative position. The control algorithms guide the follower aircraft to a desired final position along trajectories that maintain adequate observability and avoid areas prone to estimator divergence. Toward bio-inspired flow sensing, this dissertation presents an observability-based sensor placement strategy optimizing measures of flowfield observability and derives dynamic output-feedback control algorithms autonomously steering an underwater vehicle to bio-inspired behavior using a multi-modal artificial lateral line. Beyond these applications, the broader impact of this research is a general framework for using observability to assess and optimize experimental design and nonlinear control and estimation performance.en_US
dc.identifierhttps://doi.org/10.13016/M2H89G
dc.identifier.urihttp://hdl.handle.net/1903/15939
dc.language.isoenen_US
dc.subject.pqcontrolledAerospace engineeringen_US
dc.subject.pquncontrolledAdaptive Samplingen_US
dc.subject.pquncontrolledBio-inspired Sensingen_US
dc.subject.pquncontrolledMulti-vehicle Controlen_US
dc.subject.pquncontrolledNonlinear Controlen_US
dc.subject.pquncontrolledNonlinear Estimationen_US
dc.subject.pquncontrolledObservabilityen_US
dc.titleOBSERVABILITY-BASED SAMPLING AND ESTIMATION OF FLOWFIELDS USING MULTI-SENSOR SYSTEMSen_US
dc.typeDissertationen_US

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