Nonlinear model predictive control for distributed parameter systems by time–space‐coupled model reduction

RNN-LSTM-Based Model Predictive Control for a Corn-to-Sugar Process

The corn-to-sugar process is difficult to control automatically because of the complex physical and chemical phenomena involved. Because the RNN-LSTN model has been shown to handle long-term time dependencies well, this article focused on the design of a model predictive control system based on this machine learning model. Based on the historical data, we first reduced the input variable dimension through data preprocessing, data dimension reduction, sensitivity analysis, etc., and then the RNN-LSTM model, with these identified key sites as inputs, and the dextrose equivalent value as the output, was constructed. Then, through model predictive control using the locally linearized RNN-LSTM as the predictive model, the objective value of the dextrose equivalent was successfully controlled at the target value by our simulation study, in different situations of setpoint changes and disturbances. This showed the potential of applying RNN-LSTM-Based model predictive control in a corn-to-sugar process.