Non-linear model predictive control based on neural network model with modified differential evolution adapting weights

A modified differential evolution (MDE) optimisation approach is proposed to retrain the network weights of the multi-input multi-output artificial neural network (MIMO-ANN) process model. This is particularly useful for controlling the cases involving changing operating condition as well as highly non-linear processes. The utility of online retraining the network weights using MDE can further improve the predictive performances of the process model including both the possible control accuracy and the computational load reduction. A case study on a distillation column, which is a chemical non-linear process, is used to illustrate the effectiveness of the adaptive ANN based on MDE modelling and control method proposed in this paper. Significant improvements of the proposed strategy were obtained especially when assessing from the perspective of model generalisation.