  In this paper, we develop a predictive geometry control framework for
jet-based additive manufacturing (AM) based on a physics-guided recurrent
neural network (RNN) model. Because of its physically interpretable
architecture, the model's parameters are obtained by training the network
through back propagation using input-output data from a small number of layers.
Moreover, we demonstrate that the model can be dually expressed such that the
layer droplet input pattern for (each layer of) the part to be fabricated now
becomes the network parameter to be learned by back-propagation. This approach
is applied for feedforward predictive control in which the network parameters
are learned offline from previous data and the control input pattern for all
layers to be printed is synthesized. Sufficient conditions for the predictive
controller's stability are then shown. Furthermore, we design an algorithm for
efficiently implementing feedback predictive control in which the network
parameters and input patterns (for the receding horizon) are learned online
with no added lead time for computation. The feedforward control scheme is
shown experimentally to improve the RMS reference tracking error by more than
30% over the state of the art. We also experimentally demonstrate that process
uncertainties are compensated by the online learning and feedback control.
