A scalable sampling method to high-dimensional uncertainties for optimal and reinforcement learning-based controls

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dc.contributor.authorXie, Junfei
dc.contributor.authorWan, Yan
dc.contributor.authorMills, Kevin
dc.contributor.authorFilliben, James
dc.contributor.authorLewis, Frank
dc.creator.orcidhttps://orcid.org/0000-0003-4074-1615en_US
dc.creator.orcidhttps://orcid.org/0000-0003-4074-1615
dc.creator.orcidhttps://orcid.org/0000-0003-4074-1615https://orcid.org/0000-0003-4074-1615
dc.creator.orcidhttps://orcid.org/0000-0003-4074-1615
dc.creator.orcidhttps://orcid.org/0000-0003-4074-1615
dc.date.accessioned2022-03-03T01:57:05Z
dc.date.available2022-03-03T01:57:05Z
dc.date.issued2017-05-26
dc.description.abstractModern dynamical systems often operate in environments of high-dimensional uncertainties that modulate system dynamics in a complicated fashion. These high-dimensional uncertainties, non-Gaussian in many realistic scenarios, complicate real-time system analysis, design, and control tasks. In this letter, we address the scalability of computation for systems of high-dimensional uncertainties by introducing new sampling methods, the multivariate probabilistic collocation method (M-PCM), and its extension called M-PCM-orthogonal fractional factorial design (OFFD) which integrates M-PCM with the OFFDs to break the curse of dimensionality. We explore the capabilities of M-PCM and M-PCM-OFFD-based optimal control and adaptive control using the reinforcement learning approach. The analyses and simulation studies illustrate the efficiency and effectiveness of these two approaches.en_US
dc.description.abstractModern dynamical systems often operate in environments of high-dimensional uncertainties that modulate system dynamics in a complicated fashion. These high-dimensional uncertainties, non-Gaussian in many realistic scenarios, complicate real-time system analysis, design, and control tasks. In this letter, we address the scalability of computation for systems of high-dimensional uncertainties by introducing new sampling methods, the multivariate probabilistic collocation method (M-PCM), and its extension called M-PCM-orthogonal fractional factorial design (OFFD) which integrates M-PCM with the OFFDs to break the curse of dimensionality. We explore the capabilities of M-PCM and M-PCM-OFFD-based optimal control and adaptive control using the reinforcement learning approach. The analyses and simulation studies illustrate the efficiency and effectiveness of these two approaches.
dc.identifier.citationXie, J., Wan, Y., Mills, K., Filliben, J.J. and Lewis, F.L., 2017. A scalable sampling method to high-dimensional uncertainties for optimal and reinforcement learning-based controls. IEEE control systems letters, 1(1), pp.98-103.en_US
dc.identifier.citationXie, J., Wan, Y., Mills, K., Filliben, J.J. and Lewis, F.L., 2017. A scalable sampling method to high-dimensional uncertainties for optimal and reinforcement learning-based controls. IEEE control systems letters, 1(1), pp.98-103.
dc.identifier.doihttps://doi.org/10.1109/LCSYS.2017.2708598
dc.identifier.urihttps://hdl.handle.net/1969.6/90232
dc.language.isoen_USen_US
dc.language.isoen_US
dc.publisherIEEEen_US
dc.publisherIEEE
dc.subjectuncertaintyen_US
dc.subjectoptimal controlen_US
dc.subjectsystem dynamicsen_US
dc.subjectaerospace electronicsen_US
dc.subjectcomputational modelingen_US
dc.subjectsampling methodsen_US
dc.subjectscalabilityen_US
dc.subjectuncertainty
dc.subjectoptimal control
dc.subjectsystem dynamics
dc.subjectaerospace electronics
dc.subjectcomputational modeling
dc.subjectsampling methods
dc.subjectscalability
dc.titleA scalable sampling method to high-dimensional uncertainties for optimal and reinforcement learning-based controlsen_US
dc.titleA scalable sampling method to high-dimensional uncertainties for optimal and reinforcement learning-based controls
dc.typeArticleen_US
dc.typeArticle

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