Modeling and predictive control of nonlinear processes using transfer learning method

This work develops a transfer learning (TL) framework for modeling and predictive control of nonlinear systems using recurrent neural networks (RNNs) with the knowledge obtained in one process transferred to another. Specifically, uses pretrained model developed based on source domain as starting point, adapts target similar configurations. The generalization error TL-based RNN (TL-RNN) is first derived demonstrate capability process. theoretical bound that depends capacity discrepancy between domains then utilized guide development models improved transferability. Subsequently, TL-RNN prediction controller (MPC) Finally, simulation study chemical reactors via Aspen Plus Dynamics used benefits learning.