UMD Theses and Dissertations

Permanent URI for this collectionhttp://hdl.handle.net/1903/3

New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a given thesis/dissertation in DRUM.

More information is available at Theses and Dissertations at University of Maryland Libraries.

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Author: search.filters.author.Hartman, DavidSubject: search.filters.subject.Reinforcement Learning

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    SENSING AND CONTROL UNDER RESOURCE CONSTRAINTS AND UNCERTAINTY: RISK NEUTRAL AND RISK SENSITIVE APPROACHES
    (2022) Hartman, David; Baras, John S; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    In 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.