Integrated predictive iterative learning control based on updating reference trajectory for point-to-point tracking. (January 2020)

• Record Type: Journal Article
• Authors: Qiu, Weiwei; Xiong, Zhihua; Zhang, Jie; Hong, Yingdong; Li, Wanzhou
• Is Part Of: Journal of process control. Volume 85(2020)
• Page Start: 41
• Page End: 51
• Publication Date: 2020-01
• Subjects: Iterative learning control -- Model predictive control -- Point-to-point -- Updating-reference -- Convergence -- Robustness; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2019.11.003
• Languages: English
• ISSNs: 0959-1524

Abstract

Highlights: A method of point-to-point tracking control is proposed by combining ILC and MPC with updating-reference trajectory for batch processes. A batch-to-batch updating-reference trajectory, which passes through the desired points, is designed as the tracking trajectory. The updating reference relaxes the constraints for system output, which may lead to faster convergence and extensive range of application. Convergence properties are analyzed theoretically, and sufficient convergence condition of output tracking error is derived. Comparing with other point-to-point tracking control algorithms, the proposed method can perform better in robustness. Abstract: A novel control technique is proposed by combining iterative learning control (ILC) and model predictive control (MPC) with updating-reference trajectory for point-to-point tracking problem of batch process. In this paper, a batch-to-batch updating-reference trajectory, which passes through the desired points, is firstly designed as the tracking trajectory within a batch. The updating control law consists of P-type ILC part and MPC part, in which P-type ILC part can improve the performance by learning from previous executions and MPC part is used to suppress the model perturbations and external disturbances. Convergence properties of the integrated predictive iterative learning control (IPILC) are analyzed theoretically, and the sufficient convergence conditions of output tracking error are also derived for a class of linear systems. Comparing with other point-to-point tracking control algorithms, the proposed algorithm can perform better in robustness. Furthermore, updating-reference relaxes the constraints for system outputs, and it may lead to faster convergence and more extensive range of application than those of fixed-reference control algorithms. Simulation results on typical systems show the effectiveness of the proposed algorithm.