SENSING AND CONTROL UNDER RESOURCE CONSTRAINTS AND UNCERTAINTY: RISK NEUTRAL AND RISK SENSITIVE APPROACHES

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dc.contributor.advisorBaras, John Sen_US
dc.contributor.authorHartman, Daviden_US
dc.contributor.departmentElectrical Engineeringen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2022-09-17T05:31:14Z
dc.date.available2022-09-17T05:31:14Z
dc.date.issued2022en_US
dc.description.abstractIn network estimation and control systems like sensor networks or industrial robotic systems,there are often restrictions or uncertainties that must be taken into account. For example, there are often bandwidth and communication constraints on the estimators or controllers. Additionally, the dynamics model is not always known. Lastly, noise or exogenous disturbances can adversely affect your system. This thesis addresses three problems in sensing and control in both the H2 and risk-sensitivecontrol setting. The first problem stems from restrictions on the communications and battery life of sensors. Because of these restrictions, when estimating a state in a system we must cleverly schedule which sensors can be active. The second problem also stems from communication restrictions. In this setting, the sensors and actuators can only communicate with a small number of neighboring sensors. Therefore, we must solve a distributed control problem. The third problem stems from the dynamics of a system being unknown. In this regard, we must solve a control problem using simulated data instead of a fixed model. The research in this thesis, utilizes tools from optimization, estimation, control, and dynamic programming.en_US
dc.identifierhttps://doi.org/10.13016/kyqu-gh72
dc.identifier.urihttp://hdl.handle.net/1903/29177
dc.language.isoenen_US
dc.subject.pqcontrolledElectrical engineeringen_US
dc.subject.pqcontrolledSystems scienceen_US
dc.subject.pquncontrolledDistributed Controlen_US
dc.subject.pquncontrolledLinear Matrix Inequalitiesen_US
dc.subject.pquncontrolledNetwork Estimationen_US
dc.subject.pquncontrolledReinforcement Learningen_US
dc.subject.pquncontrolledRisk Sensitive Controlen_US
dc.subject.pquncontrolledSensor Schedulingen_US
dc.titleSENSING AND CONTROL UNDER RESOURCE CONSTRAINTS AND UNCERTAINTY: RISK NEUTRAL AND RISK SENSITIVE APPROACHESen_US
dc.typeDissertationen_US

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