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Data-driven plant-model mismatch estimation for dynamic matrix control systems
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DOI：10.1002/rnc.5162.png)
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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.
Keyword:
autocovariance-based method
dynamic matrix control
model predictive control
noise model parameter estimation
plant model mismatch estimation
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