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Full-Text Articles in Engineering
Control Of Fully-Actuated Aerial Manipulators And Omni-Directional Multirotors, Riley M. Mccarthy
Control Of Fully-Actuated Aerial Manipulators And Omni-Directional Multirotors, Riley M. Mccarthy
Mechanical Engineering ETDs
This thesis details the system modeling, design, control, simulation, construction, and
testing of both a fully-actuated and omni-directional multirotor aerial system created
for the primary purpose of performing active tasks with their environment. This work
verifies the capabilities of both systems through empirical testing, and demonstrates
how through the use of new control methods and physical designs multirotors can
expand their purpose from passive inspection based tasks to active contact based
tasks. These systems take advantage of newly implemented control allocation features present in the PX4 flight control software, version 1.14. The use of which makes designing controllers for such …
Data-Driven Porosity Prediction For Directed Energy Deposition, Georgia E. Kaufman
Data-Driven Porosity Prediction For Directed Energy Deposition, Georgia E. Kaufman
Electrical and Computer Engineering ETDs
Stochastic flaw formation leading to poor print quality is a major obstacle to the utility of directed energy deposition (DED), a laser and metal powder-based additive manufacturing technology for construction and repair of custom metal parts. While melt pool temperature variability is known to be a major factor in flaw formation, control schemes to decrease flaw formation are limited by a lack of physics-based models that fully and accurately describe DED. In this work, a stochastic reachability analysis with a data-driven model based on thermal images of the melt pool was conducted to determine the likelihood of violating melt pool …
Data-Driven Stochastic Optimal Control Using Hilbert Space Embeddings Of Distributions, Adam J. Thorpe
Data-Driven Stochastic Optimal Control Using Hilbert Space Embeddings Of Distributions, Adam J. Thorpe
Electrical and Computer Engineering ETDs
Autonomous systems are increasingly being deployed in complex environments subject to real-world uncertainty. For such systems, it may be exceptionally difficult or even impossible to compute a simple mathematical model of the system--for instance due to the presence of human elements, complex mechanics or system dynamics, or learning-enabled components. Data-driven control has recently gained significant attention in this area, where observations taken from the system evolution are used to compute an implicit representation of the system that is amenable to analysis and control. However, data-driven algorithms for control present new challenges, and require new insights to enable their use. The …