Data‐driven plant‐model mismatch estimation for dynamic matrix control systems. (14th September 2020)

• Record Type: Journal Article
• Authors: Xu, Xiaodong; Simkoff, Jodie M.; Baldea, Michael; Chiang, Leo H.; Castillo, Ivan; Bindlish, Rahul; Ashcraft, Brian
• Is Part Of: International journal of robust and nonlinear control. Volume 30:Number 17(2020)
• Page Start: 7103
• Page End: 7129
• Publication Date: 2020-09-14
• Subjects: autocovariance‐based method -- dynamic matrix control -- model predictive control -- noise model parameter estimation -- plant model mismatch estimation; Automatic control -- Periodicals; Control theory -- Periodicals; Nonlinear systems -- Periodicals; 629.836
• DOI: 10.1002/rnc.5162
• Languages: English
• ISSNs: 1049-8923

Abstract

Summary: This article addresses the plant‐model mismatch estimation problem for linear multiple‐input and multiple‐output systems operating under the dynamic matrix control (DMC) implementation of model predictive control. An autocovariance‐based method is proposed, aiming to identify parameter values that minimize the discrepancy between the theoretical autocovariance matrices derived from implementing the (explicit) DMC control law and the sampled autocovariance matrices calculated from operating data. We provide proof that the method results in unbiased estimates. A means for dealing with potential overfitting issues caused by the finite step response models used in DMC in practice is proposed. Several examples are presented to illustrated the theoretical developments.