A scalable sampling method to high-dimensional uncertainties for optimal and reinforcement learning-based controls
dc.contributor.author | Xie, Junfei | |
dc.contributor.author | Wan, Yan | |
dc.contributor.author | Mills, Kevin | |
dc.contributor.author | Filliben, James | |
dc.contributor.author | Lewis, Frank | |
dc.creator.orcid | https://orcid.org/0000-0003-4074-1615 | en_US |
dc.creator.orcid | https://orcid.org/0000-0003-4074-1615 | |
dc.creator.orcid | https://orcid.org/0000-0003-4074-1615https://orcid.org/0000-0003-4074-1615 | |
dc.creator.orcid | https://orcid.org/0000-0003-4074-1615 | |
dc.creator.orcid | https://orcid.org/0000-0003-4074-1615 | |
dc.date.accessioned | 2022-03-03T01:57:05Z | |
dc.date.available | 2022-03-03T01:57:05Z | |
dc.date.issued | 2017-05-26 | |
dc.description.abstract | Modern 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.abstract | Modern 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.citation | Xie, 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.citation | Xie, 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.doi | https://doi.org/10.1109/LCSYS.2017.2708598 | |
dc.identifier.uri | https://hdl.handle.net/1969.6/90232 | |
dc.language.iso | en_US | en_US |
dc.language.iso | en_US | |
dc.publisher | IEEE | en_US |
dc.publisher | IEEE | |
dc.subject | uncertainty | en_US |
dc.subject | optimal control | en_US |
dc.subject | system dynamics | en_US |
dc.subject | aerospace electronics | en_US |
dc.subject | computational modeling | en_US |
dc.subject | sampling methods | en_US |
dc.subject | scalability | en_US |
dc.subject | uncertainty | |
dc.subject | optimal control | |
dc.subject | system dynamics | |
dc.subject | aerospace electronics | |
dc.subject | computational modeling | |
dc.subject | sampling methods | |
dc.subject | scalability | |
dc.title | A scalable sampling method to high-dimensional uncertainties for optimal and reinforcement learning-based controls | en_US |
dc.title | A scalable sampling method to high-dimensional uncertainties for optimal and reinforcement learning-based controls | |
dc.type | Article | en_US |
dc.type | Article |
Files
Original bundle
1 - 1 of 1
Loading...
- Name:
- Xie_Junfei_ControlSystemsLetters.pdf
- Size:
- 394.95 KB
- Format:
- Adobe Portable Document Format
- Description:
- Article
License bundle
1 - 1 of 1
No Thumbnail Available
- Name:
- license.txt
- Size:
- 1.72 KB
- Format:
- Item-specific license agreed upon to submission
- Description: