Tuning of subspace predictive controls

V. Breschi, . and T. Bou, Hamdan and G. Merc`ere, . Tuning of subspace predictive controls. Tuning of subspace predictive controls. pp. 103-108.

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Abstract

Data-driven predictive control has recently gained increasing attention, as it makes it possible to design constrained controls directly from a set of data, without requiring an intermediate identification step. In this paper, we focus on a Subspace Predictive Control (SPC) scheme, with the aim of clarifying the sensitivity of the final closed-loop performance to its main hyperparameters, namely the length of the past horizon and the regularization penalties. Moreover, by delving deep into the structural properties of the control problem formulation, we provide a set of guidelines for the choice of such hyperparameters. The effectiveness of the resulting overall tuning strategy is assessed on two benchmark examples.

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