Detection of model-plant mismatch in closed-loop control system. (September 2017)

• Record Type: Journal Article
• Authors: Ling, Dan; Zheng, Ying; Zhang, Hong; Yang, Weidong; Tao, Bo
• Is Part Of: Journal of process control. Volume 57(2017)
• Page Start: 66
• Page End: 79
• Publication Date: 2017-09
• Subjects: Model-based control -- Internal model control -- Model-plant mismatch -- Model evaluation index; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2017.06.008
• Languages: English
• ISSNs: 0959-1524

Abstract

Highlights: A model evaluation index is proposed to detect process model-plant mismatch in closed-loop system. The index is obtained from routine operation data without introducing any external perturbations when the disturbance model order is less than 2 or the process time delay is large enough. The index is independent of the controller tuning and changes in disturbance model. The proposed method is tested on the systems with PI, LQ and unconstrained MPC controllers. Abstract: The performance of model-based control systems depends a lot on the process model quality, hence the process model-plant mismatch is an important factor degrading the control performance. In this paper, a new methodology based on a process model evaluation index is proposed for detecting process model mismatch in closed-loop control systems. The proposed index is the ratio between the variance of the disturbance innovation and that of the model quality variable. The disturbance innovations are estimated from the routine operation data by an orthogonal projection method. The model quality variable can be obtained using the closed-loop data and the disturbance model estimated by adaptive Least absolute shrinkage and selection operator (Lasso) method. When the order of the disturbance model is less than 2 or the process time delay is large enough, no external perturbations are required. Besides, the proposed index is independent of the controller tuning and insensitive to the changes in disturbance model, which indicates that the proposed method can isolate the process model-plant mismatch from other factors affecting the overall control performance. Three systems with proportional integral (PI) controller, linear quadratic (LQ) controller and unconstrained model predictive control (MPC) respectively are presented as examples to verify the effectiveness of the proposed technique. Besides, Tennessee Eastman process shows the proposed method is able to detect process model mismatch of nonlinear systems.