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On the impact of regularization in data-driven predictive control
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On the impact of regularization in data-driven predictive control
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Model predictive control (MPC) is a control strategy widely used in industrial applications. However, its implementation typically requires a mathematical model of the system being controlled, which can be a time-consuming and expensive task. Data-driven predictive control (DDPC) methods offer an alternative approach that does not require an explicit mathematical model, but instead optimize the control policy directly from data. In this paper, we study the impact of two different regularization penalties on the closed-loop performance of a recently introduced data-driven method called $\gamma$-DDPC. Moreover, we discuss the tuning of the related coefficients in different data and noise scenarios, to provide some guidelines for the end user.
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Cited by 1 Pith paper
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Projection-Regularized Indirect Data-Driven Predictive Control
Projection-regularized indirect DPC reduces EIV prediction error versus SPC, adapts via covariance blending, and yields high-probability recursive feasibility and ISpS under martingale noise bounds.
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