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Optimal feedback control of batch self-assembly processes using dynamic programming
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DOI：10.1016/j.jprocont.2020.01.013.png)
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Abstract
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This paper reviews a previously-reported methodology for establishing feedback control of self-assembly. The methodology combines dimension reduction, supervised learning, and dynamic programming to obtain an optimal feedback control policy for reaching a desired assembled state. Sampled data are used in calculating the optimal feedback policy; this data can be generated using a predictive model (i.e. simulated data) or using experimental data. The control strategy is 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) 2020 Elsevier Ltd. All rights reserved.
Keywords:
Dynamic programming
Material systems
Markov decision processes
Closed-loop control
Reduced-order models
Learning
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