Journal
IFAC PAPERSONLINE
Volume 52, Issue 1, Pages 1-9Publisher
ELSEVIER
DOI: 10.1016/j.ifacol.2019.06.029
Keywords
Dynamic programming; material systems; Markov decision processes; closed-loop control; reduced-order models; learning
Categories
Funding
- Consortium for Risk Evaluation with Stakeholder Participation (CRESP)
- Nuclear Energy University Program (NEUP)
- Georgia Research Alliance
- Cecil J. Pete Silas Endowment
- National Science Foundation [1124678]
- Div Of Civil, Mechanical, & Manufact Inn
- Directorate For Engineering [1124678] Funding Source: National Science Foundation
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This conference paper updates a previously-reported methodology for establishing feedback control of self-assembly (Griffin et al. (2016b)). The methodology combines dimension reduction, supervised learning, and dynamic programming to obtain an optimal feedback control policy for reaching a desired assembled state. The strategy is further demonstrated, with both simulation and experimental results, for two applications: control of colloidal assembly (to produce perfect colloidal crystals) and control of crystallization from solution (to produce crystals of desired average size). (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
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