(29a) Robust Artificial Neural Networks for Nonlinear Model Predictive Control of Multiscale Stochastic Systems
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Sunday, November 10, 2019 - 3:30pm to 3:49pm
In our previous work , we used ANN models to predict the responses of a stochastic multiscale model of thin film formation by Chemical Vapour Deposition (CVD) . The ANN models were subsequently incorporated into a shrinking horizon nonlinear model predictive control scheme. The predicted responses were roughness and growth rate while the manipulated variables were substrate temperature and bulk precursor mole fraction. It was demonstrated that the ANNs can efficiently provide accurate predictions of the systemâs observables and reject disturbances not seen during training. However, uncertainty was not considered when generating the data from the stochastic multiscale model.
In this work, we propose training robust ANNs with distributed weights under parametric uncertainty to efficiently generate predictions that account for the variability in the observables. As a case study, we employ the aforementioned CVD system  to generate the training datasets under parametric uncertainty. We obtain the distributions and the statistical moments of the observables and subsequently incorporate the robust ANNs into a nonlinear model predictive control framework. The predicted trajectories are subsequently validated against the multiscale CVD model. Rather than obtaining only the mean response of the observables to the manipulated variables, we predict several possible time series of the observables and provide bounds, thereby enabling robust control of the multiscale stochastic system in the presence of parametric uncertainty.
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