Data‐driven plant‐model mismatch estimation for dynamic matrix control systems

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 value...

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Bibliographic Details
Published in:International journal of robust and nonlinear control 2020-11, Vol.30 (17), p.7103-7129
Main Authors: Xu, Xiaodong, Simkoff, Jodie M., Baldea, Michael, Chiang, Leo H., Castillo, Ivan, Bindlish, Rahul, Ashcraft, Brian
Format: Article
Language:English
Subjects:
autocovariance‐based method
Control systems
Control theory
dynamic matrix control
Matrix methods
model predictive control
noise model parameter estimation
Parameter identification
plant model mismatch estimation
Predictive control
Step response
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Summary: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.
ISSN:1049-8923
1099-1239
DOI:10.1002/rnc.5162