An integrated model predictive control strategy for batch processes
A novel integrated model predictive control (MPC) strategy with model identification for batch processes is proposed in this paper. It systematically integrates batch-axis information and time-axis information into one uniform frame. The control law is obtained through the solution of a MPC optimization with time-varying prediction horizon, which leads to superior tracking performance and robustness against disturbance and uncertainty. Moreover, the model identification with online updated parameter algorithm is employed to eliminate the model-plant mismatch and match the real plant better. Next, the convergence analysis of the proposed integrated model predictive control system is given rigorous description and proof. Lastly, the effectiveness of the proposed method is verified by an example.