An integrated iterative learning control strategy with model identification and dynamic R-parameter for batch processes
Abstract An integrated iterative learning control strategy with model identification and dynamic R-parameter is proposed in this paper. It systematically integrates discrete-time (batch-axis) information and continuous-time (time-axis) information into one uniform frame, namely the iterative learning controller in the domain of batch-axis, while a PID controller (PIDC) in the domain of time-axis. As a result, 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. Moreover, the technologies of model identification and dynamic R-parameter are employed to make zero-error tracking possible. Next, the convergence and tracking performance of the proposed learning control system are firstly given rigorous description and proof. Lastly, the effectiveness of the proposed method is verified by examples.