Data-enabled predictive control with instrumental variables

the direct equivalence with subspace predictive control

Conference paper (2022)

Authors

J.W. van Wingerden Team Jan-Willem van Wingerden - Mechanical, Maritime and Materials Engineering
S.P. Mulders Team Jan-Willem van Wingerden - Mechanical, Maritime and Materials Engineering
R.T.O. Dinkla Team Jan-Willem van Wingerden - Mechanical, Maritime and Materials Engineering
T.A.E. Oomen Team Jan-Willem van Wingerden - Mechanical, Maritime and Materials Engineering
M.H.G. Verhaegen Team Michel Verhaegen - Mechanical, Maritime and Materials Engineering
Research Group
Team Jan-Willem van Wingerden (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2022 J.W. van Wingerden, S.P. Mulders, R.T.O. Dinkla, T.A.E. Oomen, M.H.G. Verhaegen
DOI
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https://doi.org/10.1109/CDC51059.2022.9992824
Measurement uncertainty Instruments Noise measurement Predictive control Prediction algorithms Mathematical models Parametric statistics
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Published Date

2022

Language

English

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Delft Center for Systems and Control

Research Group

Team Jan-Willem van Wingerden

Abstract

Direct data-driven control has attracted substantial interest since it enables optimization-based control without the need for a parametric model. This paper presents a new Instrumental Variable (IV) approach to Data-enabled Predictive Control (DeePC) that results in favorable noise mitigation properties, and demonstrates the direct equivalence between DeePC and Subspace Predictive Control (SPC). The methodology relies on the derivation of the characteristic equation in DeePC along the lines of subspace identification algorithms. A particular choice of IVs is presented that is uncorrelated with future noise, but at the same time highly correlated with the data matrix. A simulation study demonstrates the improved performance of the proposed algorithm in the presence of process and measurement noise.