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dc.contributor.advisorTheodorou, Evangelos A.
dc.contributor.authorWilliams, Grady Robert
dc.date.accessioned2020-05-20T16:57:06Z
dc.date.available2020-05-20T16:57:06Z
dc.date.created2019-05
dc.date.issued2019-03-21
dc.date.submittedMay 2019
dc.identifier.urihttp://hdl.handle.net/1853/62666
dc.description.abstractThis thesis presents a new approach for stochastic model predictive (optimal) control: model predictive path integral control, which is based on massive parallel sampling of control trajectories. We first show the theoretical foundations of model predictive path integral control, which are based on a combination of path integral control theory and an information theoretic interpretation of stochastic optimal control. We then apply the method to high speed autonomous driving on a 1/5 scale vehicle and analyze the performance and robustness of the method. Extensive experimental results are used to identify and solve key problems relating to robustness of the approach, which leads to a robust stochastic model predictive control algorithm capable of consistently pushing the limits of performance on the 1/5 scale vehicle.
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.publisherGeorgia Institute of Technology
dc.subjectStochastic optimal control
dc.subjectAutonomous driving
dc.titleModel predictive path integral control: Theoretical foundations and applications to autonomous driving
dc.typeDissertation
dc.description.degreePh.D.
dc.contributor.departmentComputer Science
thesis.degree.levelDoctoral
dc.contributor.committeeMemberRehg, James M.
dc.contributor.committeeMemberEgerstedt, Magnus
dc.contributor.committeeMemberBoots, Byron
dc.contributor.committeeMemberTodorov, Emanuel
dc.date.updated2020-05-20T16:57:06Z


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