Electrical Engineering and Systems Science > Systems and Control
[Submitted on 18 Aug 2021 (v1), last revised 13 Sep 2021 (this version, v2)]
Title:Learning explicit predictive controllers: theory and applications
View PDFAbstract:In this paper, we deal with data-driven predictive control of linear time-invariant (LTI) systems. Specifically, we show for the first time how explicit predictive laws can be learnt directly from data, without needing to identify the system to control. To this aim, we resort to the Willems' fundamental lemma and we derive the explicit formulas by suitably elaborating the constrained optimization problem under investigation. The resulting optimal controller turns out to be a piecewise affine system coinciding with the solution of the original model-based problem in case of noiseless data. Such an equivalence is proven to hold asymptotically also in presence of measurement noise, thus making the proposed method a computationally efficient (but model-free) alternative to the state of the art predictive controls. The above statements are further supported by numerical simulations on three benchmark examples.
Submission history
From: Valentina Breschi [view email][v1] Wed, 18 Aug 2021 23:50:15 UTC (501 KB)
[v2] Mon, 13 Sep 2021 12:53:32 UTC (501 KB)
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