Data-driven two-dimensional integrated control for nonlinear batch processes

Abstract

Two-dimensional control has been considered as an effective strategy to accomplish high-accuracy tracking for batch processes because of its excellent learning ability and time-domain stability. However, being a model-based control method, the performance of the two-dimensional control system will inevitably decrease due to unknown uncertainties or unmodeled dynamics. In addition, the high computational cost and complex design process of the control system severely limit its application in batch processes. For this reason, this paper proposes a new data-driven two-dimensional integrated control (DDTDIC) method for nonlinear batch processes. In the presented control scheme, the P-type iterative learning control (ILC) is adopted along the batch-axis to ensure the convergence of the system, and the proportional-integral-differential (PID) control is used in the time-axis to reject the influence of real-time disturbance. The parameters of the PID controller are obtained by utilizing the virtual reference feedback tuning (VRFT) method. The entire design process of the control system only requires the input and output (I/O) data of the batch processes and does not depend on any explicit model information. The simulation results show that compared with the ILC and the two-dimensional control, the presented control method not only has faster convergence speed and smaller tracking error, but also the computational efficiency is improved by more than 40% and 50% respectively.