Abstract
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).
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