Autocovariance-based MPC model mismatch estimation for systems with measurable disturbances. (July 2017)

• Record Type: Journal Article
• Authors: Wang, Siyun; Simkoff, Jodie M.; Baldea, Michael; Chiang, Leo H.; Castillo, Ivan; Bindlish, Rahul; Stanley, David B.
• Is Part Of: Journal of process control. Volume 55(2017)
• Page Start: 42
• Page End: 54
• Publication Date: 2017-07
• Subjects: Model predictive control -- Plant-model mismatch -- Autocovariance; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2017.03.002
• Languages: English
• ISSNs: 0959-1524

Abstract

Abstract : Highlights: New autocovariance-based method for quantifying MPC plant-model mismatch. Analysis based on routine closed-loop operating data. Applicable to parametric and non-parametric models. Abstract: In this paper, we propose a novel autocovariance-based plant-model mismatch estimation approach for linear MPC MIMO control loops with changing setpoints and measurable disturbances. Assuming a noise model is available and that there are of periods of operating data where the active set of the controller is fixed and the plant-model mismatch is invariant, we establish an explicit relation between the autocovariance matrices of the mean-centered process outputs and the plant-model mismatch. We then formulate the mismatch estimation problem as an optimization aimed at minimizing the difference between the theoretical autocovariance, computed from the established relation, and actual output autocovariances, calculated from the plant data. We elaborate our results for step-response models typically used in MPC, as well as for parametric (transfer function models) in both continuous time and discrete time. A simulation case study for an unconstrained MPC controller with measurable disturbances is used to illustrate the theoretical results.