Point-to-Point Tracking of Integrated Predictive Iterative Learning Control By Using Updating-Reference and CARIMA Model

Qiu, WW; Xiong, ZH; Li, WZ; Zhang, J

doi:10.1109/CCDC.2016.7531887

Point-to-Point Tracking of Integrated Predictive Iterative Learning Control By Using Updating-Reference and CARIMA Model

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Abstract

For point-to-point tracking control problem of batch process, a novel design method based on Controlled Auto-regressive Integrated Moving Average (CARIMA) model and updating-reference is proposed in this paper. In the proposed approach, integrated predictive iterative learning control (IPILC) is used for the trajectory tracking control. Comparing with other point-to-point tracking control algorithms, the proposed control scheme performs better in robustness, and reduces the computation load which occurs in those algorithms based on the lifted model for non-Lyapunov-stable systems. Furthermore, updating-reference relaxes the constraints for system outputs and leads to faster convergence than the fixed-reference control algorithms. Simulation results on typical systems show the effectiveness of the proposed algorithm.