Professor Weiguo Xie

Model Predictive Control (MPC) has been widely used in process industries. However, in the real world most systems exhibit nonlinear dynamics, rendering the application of linear controllers. In order to apply MPC for nonlinear distributed-parameter systems with unknown dynamics, as a “black-box” system, a data-driven model reduction-based feedforward artificial neural network (ANN) approach has been developed for MPC control. An off-line model reduction technique, the proper orthogonal decomposition (POD) method, is first applied to extract accurate non-linear low-order models from the non-linear dynamic large-scale distributed system. Then a series of successive feedforward ANNs are trained based on the time coefficients of POD basis functions to obtain the model for the system.