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Data‐driven plant‐model mismatch estimation for dynamic matrix control systems
Author(s) -
Xu Xiaodong,
Simkoff Jodie M.,
Baldea Michael,
Chiang Leo H.,
Castillo Ivan,
Bindlish Rahul,
Ashcraft Brian
Publication year - 2020
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/rnc.5162
Subject(s) - overfitting , autocovariance , computer science , matrix (chemical analysis) , control theory (sociology) , control (management) , algorithm , mathematical optimization , mathematics , machine learning , artificial intelligence , mathematical analysis , materials science , fourier transform , artificial neural network , composite material
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.