Data-Driven Plant-Model Mismatch Quantification in Input-Constrained Linear MPC. Issue 7 (2016)

• 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: IFAC-PapersOnLine. Volume 49:Issue 7(2016)
• Page Start: 25
• Page End: 30
• Publication Date: 2016
• Subjects: Model Predictive Control -- Plant-model Mismatch; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2016.07.211
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: In this paper, we present a novel data-driven approach for estimating plant-model mismatch for linear MIMO systems operating under constrained MPC. We begin with analyzing the closed-loop plant data; under the assumption that changes in the active set of constraints of the controller are due to (low frequency) setpoint changes, we separate the data into a finite number of fixed active set (FAS) subsets, each of which features a time-invariant active set of MPC constraints. We establish an explicit relationship relating the magnitude of plant-model mismatch to the autocovariance of the system output in the FAS case, while accounting for changes in the setpoint value. The mismatch estimation problem is then formulated as a global optimization calculation, aimed at minimizing the discrepancy between the autocovariance estimated using this theoretical tool, and the autocovariance of plant outputs computed from operating data for each FAS subset. A chemical process case study is presented to illustrate the effectiveness of the approach.