Data-Driven, Non-Parametric Model Reference Adaptive Control Methods for Autonomous Underwater Vehicles

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dc.contributor.authorOesterheld, Derek I.en
dc.contributor.committeechairStilwell, Daniel J.en
dc.contributor.committeememberBrizzolara, Stefanoen
dc.contributor.committeememberKurdila, Andrew J.en
dc.contributor.departmentElectrical Engineeringen
dc.date.accessioned2023-11-04T08:00:38Zen
dc.date.available2023-11-04T08:00:38Zen
dc.date.issued2023-11-03en
dc.description.abstractThis thesis details the implementation of two adaptive controllers on autonomous underwater vehicle(AUV) attitude dynamics starting from the standard six degree-of-freedom dynamic model. I apply two model reference adaptive control (MRAC) algorithms which make use of kernel functions for learning functional uncertainty present in the system dynamics. The first method extends recent results on model reference adaptive control using reproducing kernel Hilbert space (RKHS) learning techniques for some general cases of multi-input systems. The first controller design is a model reference adaptive controller (MRAC) based on a vector- valued RKHS that is induced by operator-valued kernels. This paper formulates a model reference adaptive control strategy based on a dead zone robust modification, and derives conditions for the ultimate boundedness of the tracking error in this case. The second controller is an implementation of the Gaussian Process MRAC developed by Chowdhary, et al. I discuss the method of each of these algorithms before contrasting the underlying theoretical structure of each algorithm. Finally, I provide a comparison of each algorithm's performance on the six degree-of-freedom dynamic model of the Virginia Tech 690 AUV and provide field trial results for the RKHS based MRAC implementation.en
dc.description.abstractgeneralThis thesis details the implementation of two algorithms which control the attitude of an autonomous underwater vehicle. Rather than developing detailed dynamic models of the vehicles as is performed in classical control methods, each of these implementations only makes assumptions that the unknown portions of the dynamic models can be represented by a broad class of functions defined by a mathematical structure called a reproducing kernel Hilbert Space. Each algorithm implements learning techniques using the theory of reproducing kernel Hilbert spaces to bound the error between the vehicle attitude and the commanded vehicle attitude. One algorithm, called RKHS MRAC, implements an adaptive update law based on the attitude error to improve the controller performance. The second algorithm, called GP MRAC, uses estimated vehicle rotational accelerations and statistical learning methods to approximate the unknown function. Each of these methods is compared in theory and in a vehicle simulation. The RKHS MRAC is additionally demonstrated in field trial results.en
dc.description.degreeMaster of Scienceen
dc.format.mediumETDen
dc.identifier.othervt_gsexam:38551en
dc.identifier.urihttp://hdl.handle.net/10919/116633en
dc.language.isoenen
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectAdaptive Controlen
dc.subjectKernel methodsen
dc.subjectAutonomous Underwater Vehiclesen
dc.titleData-Driven, Non-Parametric Model Reference Adaptive Control Methods for Autonomous Underwater Vehiclesen
dc.typeThesisen
thesis.degree.disciplineElectrical Engineeringen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen

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