A transfer predictive control method based on inter-domain mapping learning with application to industrial roasting process

As a critical variable in the roasting process, the roasting temperature has a significant influence on operating conditions. Model predictive control (MPC) provides a path to stabilize the roasting temperature. However, process data collected at different periods usually follow different distributions due to the fluctuation of feed composition for the roasting process, result in a model mismatch on online control. For this reason, a transfer predictive control method based on inter-domain mapping learning (IDML-MPC) is proposed. The proposed method first treat historical and online data as two domains. Then, a distribution mapping function from one domain to another domain is learned to make the distribution of the historical data follow that of the online data. Finally, an accurate online prediction model is built, roasting temperature control is achieved by minimizing the cost function with respect to the predicted value and the control input. The effectiveness of the proposed method is demonstrated by comparative experiments based on a numerical example and a simulation platform of the roasting process. Experimental results compared with some state-of-the-art methods show that it is necessary to take into account the distribution differences between historical data and online data when production conditions change. The IDML-MPC improved the control performance for the roasting temperature with an average 56.98% reduction in the root mean square error.