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Data‐driven plant‐model mismatch estimation for dynamic matrix control systems.
- Source :
-
International Journal of Robust & Nonlinear Control . 11/25/2020, Vol. 30 Issue 17, p7103-7129. 27p. - Publication Year :
- 2020
-
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. [ABSTRACT FROM AUTHOR]
- Subjects :
- *MATRICES (Mathematics)
*PREDICTION models
*PARAMETER estimation
Subjects
Details
- Language :
- English
- ISSN :
- 10498923
- Volume :
- 30
- Issue :
- 17
- Database :
- Academic Search Index
- Journal :
- International Journal of Robust & Nonlinear Control
- Publication Type :
- Academic Journal
- Accession number :
- 146471463
- Full Text :
- https://doi.org/10.1002/rnc.5162