文摘
A novel integrated model predictive iterative learning control (MPILC) strategy with dynamic R-parameter for batch processes is proposed in this paper. It systematically integrates batch-axis information and time-axis information into one uniform frame, namely the iterative learning controller (ILC) in the domain of batch-axis, while a model predictive controller (MPC)with time-varying prediction horizon in the domain of time-axis. The operation policy of batch process can be regulated during one batch, which leads to superior tracking performance and better robustness against disturbance and uncertainty. Both model identification and dynamic R-parameter are employed to eliminate the model-plant mismatch and make zero-error tracking possible. The convergence and tracking performance of the proposed integrated model predictive learning control system are given rigorous description and proof.