Kavli Affiliate: Biao Huang
| First 5 Authors: Malika Sader, Yibo Wang, Dexian Huang, Chao Shang, Biao Huang
| Summary:
As a useful and efficient alternative to generic model-based control scheme,
data-driven predictive control is subject to bias-variance trade-off and is
known to not perform desirably in face of uncertainty. Through the connection
between direct data-driven control and subspace predictive control, we gain
insight into the reason being the lack of causality as a main cause for high
variance of implicit prediction. In this article, we seek to address this
deficiency by devising a novel causality-informed formulation of direct
data-driven control. Built upon LQ factorization, an equivalent two-stage
reformulation of regularized data-driven control is first derived, which bears
clearer interpretability and a lower complexity than generic forms. This paves
the way for deriving a two-stage causality-informed formulation of data-driven
predictive control, as well as a regularized form that balances between control
cost minimization and implicit identification of multi-step predictor. Since it
only calls for block-triangularization of a submatrix in LQ factorization, the
new causality-informed formulation comes at no excess cost as compared to
generic ones. Its efficacy is investigated based on numerical examples and
application to model-free control of a simulated industrial heating furnace.
Empirical results corroborate that the proposed method yields obvious
performance improvement over existing formulations in handling stochastic noise
and process nonlinearity.
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